PhD Chapter 1 - PatchSize
Emanuele Giacomuzzo
2024-02-07 16:46
Somewhere there’s analysis small and large patches special
## [1] "!! low !! (this document showcases only the analysis done for the low disturbance)"Design
Parameters
# R markdown
start_time = Sys.time()
knitr::opts_chunk$set(message = FALSE,
cache = FALSE,
autodep = FALSE)
recompute_lengthy_analyses = FALSE #Lengthy analyses have been suppressed. Set recompute_analyses = TRUE to redo them. # Disturbance & resource flows
disturbance_levels = c("low", "high")
n_disturbance_levels = length(disturbance_levels)
resource_flow_days = c(5, 9, 13, 17, 21, 25)
first_resource_flow = resource_flow_days[1]# Sampling
total_frames = 125
volume_recorded_μl = 34.4
time_points = 0:7
time_points_without_t0 = 1:7
time_point_names = c("t0", "t1", "t2", "t3", "t4", "t5", "t6", "t7")
sampling_days = c(0, 4, 8, 12, 16, 20, 24, 28)
first_time_point = 0
last_time_point = length(sampling_days) - 1
n_time_points = last_time_point + 1
nr_videos = c(12, 1, 1, 1, 1, 1, 2, 2) #Videos taken for each time point for each culture. At t0 we took 12 videos of the large bottle from which we started the cultures. Write why 2 at the end.
videos_taken = data.frame(time_point = 0 : 7,
nr_videos = c(12, 1, 1, 1, 1, 1, 2, 2))
n_videos_taken_t0 = nr_videos[1]
time_point_day = data.frame(
time_point = first_time_point:last_time_point,
day = sampling_days,
video_replicates = nr_videos
)
videos_to_take_off = data.frame(culture_ID = NA,
time_point = NA,
file = NA) %>%
add_row(culture_ID = 137-110, time_point = 7, file = 137) %>%
slice(-1)
n_cultures = 110
total_number_of_video_rows = sum(nr_videos * n_cultures)# Protists
protist_species = c("Ble", "Cep", "Col", "Eug", "Eup", "Lox", "Pau", "Pca", "Spi", "Spi_te", "Tet")
protist_species_indiv_per_volume = paste0(protist_species, "_indiv_per_volume")
protist_species_indiv_per_ml = paste0(protist_species, "_indiv_per_ml")
protist_species_dominance = paste0(protist_species_indiv_per_ml, "_dominance")
protist_species_total = paste0(protist_species, "_tot_indiv")
n_protist_species = length(protist_species)
first_protist = protist_species[1]
last_protist = protist_species[n_protist_species]
species_IDD_with_13_threshold = c("Col", "Eug", "Eup", "Lox", "Pau", "Pca", "Spi_te", "Tet")
species_IDD_with_13_threshold_indiv_per_volume = paste0(species_IDD_with_13_threshold, "_indiv_per_volume")
species_IDD_with_40_threshold = c("Ble", "Cep", "Spi")
species_IDD_with_40_threshold_indiv_per_volume = paste0(species_IDD_with_40_threshold, "_indiv_per_volume")# Patches
patches_to_take_off = 60 #Culture ID = 60 as it was spilled (small isolated, high disturbance, system nr = 40)
culture_info = read.csv(here("data", "culture_info.csv"), header = TRUE)
columns_patches = c("time_point",
"day",
"culture_ID",
"system_nr",
"disturbance",
"patch_type",
"patch_size",
"patch_size_ml",
"size_connected_patch",
"metaecosystem",
"metaecosystem_type")
columns_treatments = columns_patches[!columns_patches %in% c("system_nr", "culture_ID")]
variables_patches = c("bioarea_mm2_per_ml",
"bioarea_tot_mm2",
"indiv_per_ml",
"indiv_tot",
"species_richness",
"shannon",
"simpson",
"inv_simpson",
"evenness_pielou",
"median_body_area_µm2",
paste0(protist_species, "_indiv_per_ml"),
paste0(protist_species, "_tot_indiv"),
paste0(protist_species_indiv_per_ml, "_dominance"))
baseline_columns = paste0("baseline_", variables_patches)
patch_types_ordered = c("Small isolated",
"Small connected to small",
"Small connected to large",
"Medium isolated",
"Medium connected to medium",
"Large isolated",
"Large connected to large",
"Large connected to small")
treatments_and_controls = data.frame(
treatment = c(
"Small connected to small",
"Small connected to large",
"Medium connected to medium",
"Large connected to large",
"Large connected to small"
),
control = c(
"Small isolated",
"Small isolated",
"Medium isolated",
"Large isolated",
"Large isolated"
)
)
n_treatments = length(unique(treatments_and_controls$treatment))
n_controls = length(unique(treatments_and_controls$control))
n_replicates = 5
n_patch_types = 8# Size classes
n_size_classes = 12
columns_classes = c(columns_patches,
"size_class_n",
"mean_class_area_µm2")# Meta-ecosystems
metaecosystems_to_take_off = culture_info %>%
filter(culture_ID %in% patches_to_take_off) %>%
pull(system_nr) %>%
unique
system_nr_metaecosystems = culture_info %>%
filter(metaecosystem == "yes") %>%
pull(system_nr) %>%
unique
n_metaecosystems = length(system_nr_metaecosystems)
variables_metaecos = c(
"total_metaecosystem_bioarea_mm2",
"jaccard_index",
"bray_curtis",
"beta_spatial_turnover",
"beta_nestedness",
"beta_total",
"metaecosystem_richness")
metaecosystem_types_ordered = c(
"Small-Small meta-ecosystem",
"Medium-Medium meta-ecosystem",
"Medium-Medium isolated",
"Large-Large meta-ecosystem",
"Small-Large meta-ecosystem",
"Small-Large isolated")# Plotting
axis_names = data.frame(variable = NA,
axis_name= NA) %>%
add_row(variable = "day", axis_name = "Time (day)") %>%
add_row(variable = "patch_size_ml", axis_name = "Patch size (ml)") %>%
add_row(variable = "log_size_class", axis_name = "Log size (μm2)") %>%
add_row(variable = "class_indiv_per_µl", axis_name = "Density (ind/ml)") %>%
add_row(variable = "bioarea_mm2_per_ml", axis_name = "Bioarea density (mm²/ml)") %>%
add_row(variable = "bioarea_mm2_per_ml_d", axis_name = "Bioarea Density ES") %>%
add_row(variable = "bioarea_tot", axis_name = "Tot Bioarea (mm²)") %>%
add_row(variable = "total_metaecosystem_bioarea_mm2", axis_name = "Tot Bioarea (mm²)") %>%
add_row(variable = "species_richness", axis_name = "Species Richness") %>%
add_row(variable = "species_richness_d", axis_name = "Species Richness ES") %>%
add_row(variable = "mean_richness", axis_name = "mean α Diversity (Shannon)") %>%
add_row(variable = "mean_shannon", axis_name = "mean α Diversity (Shannon)") %>%
add_row(variable = "shannon", axis_name = "Biodiversity (Shannon)") %>%
add_row(variable = "shannon_d", axis_name = "Biodiversity ES (Shannon ES)") %>%
add_row(variable = "bray_curtis", axis_name = "β Diversity (Bray-Curtis)") %>%
add_row(variable = "beta_spatial_turnover", axis_name = "Turn over (Simpson pair-wise dissimilarity)") %>%
add_row(variable = "beta_nestedness", axis_name = "Nestedness (nestedness-fraction of Sorensen)") %>%
add_row(variable = "beta_total", axis_name = "Tot β Diversity (Sorensen)") %>%
add_row(variable = "metaecosystem_richness", axis_name = "γ Diversity (Species Richness)") %>%
add_row(variable = "indiv_per_ml", axis_name = "Abundance (ind/ml)") %>%
add_row(variable = "indiv_per_ml_d", axis_name = "Abundance ES") %>%
add_row(variable = "median_body_area_µm2", axis_name = "Median body size (µm²)") %>%
add_row(variable = "median_body_area_µm2_d", axis_name = "Median Body Size ES") %>%
add_row(variable = "Ble_indiv_per_ml", axis_name = "Ble Density (ind/ml)") %>%
add_row(variable = "Cep_indiv_per_ml", axis_name = "Cep Density (ind/ml)") %>%
add_row(variable = "Col_indiv_per_ml", axis_name = "Col Density (ind/ml)") %>%
add_row(variable = "Eug_indiv_per_ml", axis_name = "Eug Density (ind/ml)") %>%
add_row(variable = "Eup_indiv_per_ml", axis_name = "Eup Density (ind/ml)") %>%
add_row(variable = "Lox_indiv_per_ml", axis_name = "Lox Density (ind/ml)") %>%
add_row(variable = "Pau_indiv_per_ml", axis_name = "Pau Density (ind/ml)") %>%
add_row(variable = "Pca_indiv_per_ml", axis_name = "Pca Density (ind/ml)") %>%
add_row(variable = "Spi_indiv_per_ml", axis_name = "Spi Density (ind/ml)") %>%
add_row(variable = "Spi_te_indiv_per_ml", axis_name = "Spi te Density (ind/ml)") %>%
add_row(variable = "Tet_indiv_per_ml", axis_name = "Tet Density (ind/ml)") %>%
add_row(variable = "Ble_indiv_per_ml_d", axis_name = "Ble Density ES") %>%
add_row(variable = "Cep_indiv_per_ml_d", axis_name = "Cep Density ES") %>%
add_row(variable = "Col_indiv_per_ml_d", axis_name = "Col Density ES") %>%
add_row(variable = "Eug_indiv_per_ml_d", axis_name = "Eug Density ES") %>%
add_row(variable = "Eup_indiv_per_ml_d", axis_name = "Eup Density ES") %>%
add_row(variable = "Lox_indiv_per_ml_d", axis_name = "Lox Density ES") %>%
add_row(variable = "Pau_indiv_per_ml_d", axis_name = "Pau Density ES") %>%
add_row(variable = "Pca_indiv_per_ml_d", axis_name = "Pca Density ES") %>%
add_row(variable = "Spi_indiv_per_ml_d", axis_name = "Spi Density ES") %>%
add_row(variable = "Spi_te_indiv_per_ml_d", axis_name = "Spi te Density ES") %>%
add_row(variable = "Tet_indiv_per_ml_d", axis_name = "Tet Density ES") %>%
add_row(variable = "Ble_indiv_per_ml_dominance", axis_name = "Ble Dominance (%)") %>%
add_row(variable = "Cep_indiv_per_ml_dominance", axis_name = "Cep Dominance (%)") %>%
add_row(variable = "Col_indiv_per_ml_dominance", axis_name = "Col Dominance (%)") %>%
add_row(variable = "Eug_indiv_per_ml_dominance", axis_name = "Eug Dominance (%)") %>%
add_row(variable = "Eup_indiv_per_ml_dominance", axis_name = "Eup Dominance (%)") %>%
add_row(variable = "Lox_indiv_per_ml_dominance", axis_name = "Lox Dominance (%)") %>%
add_row(variable = "Pau_indiv_per_ml_dominance", axis_name = "Pau Dominance (%)") %>%
add_row(variable = "Pca_indiv_per_ml_dominance", axis_name = "Pca Dominance (%)") %>%
add_row(variable = "Spi_indiv_per_ml_dominance", axis_name = "Spi Dominance (%)") %>%
add_row(variable = "Spi_te_indiv_per_ml_dominance", axis_name = "Spi te Dominance (%)") %>%
add_row(variable = "Tet_indiv_per_ml_dominance", axis_name = "Tet Dominance (%)") %>%
add_row(variable = "Ble_indiv_per_ml_dominance_d", axis_name = "Ble Dominance ES") %>%
add_row(variable = "Cep_indiv_per_ml_dominance_d", axis_name = "Cep Dominance ES") %>%
add_row(variable = "Col_indiv_per_ml_dominance_d", axis_name = "Col Dominance ES") %>%
add_row(variable = "Eug_indiv_per_ml_dominance_d", axis_name = "Eug Dominance ES") %>%
add_row(variable = "Eup_indiv_per_ml_dominance_d", axis_name = "Eup Dominance ES") %>%
add_row(variable = "Lox_indiv_per_ml_dominance_d", axis_name = "Lox Dominance ES") %>%
add_row(variable = "Pau_indiv_per_ml_dominance_d", axis_name = "Pau Dominance ES") %>%
add_row(variable = "Pca_indiv_per_ml_dominance_d", axis_name = "Pca Dominance ES") %>%
add_row(variable = "Sp_indiv_per_mli_dominance_d", axis_name = "Spi Dominance ES") %>%
add_row(variable = "Spi_te_indiv_per_ml_dominance_d", axis_name = "Spi te Dominance ES") %>%
add_row(variable = "Tet_indiv_per_ml_dominance_d", axis_name = "Tet Dominance ES") %>%
add_row(variable = "dominance", axis_name = "Dominance (%)") %>%
add_row(variable = "log_abundance", axis_name = "Log Abundance + 1 (ind/mm²)") %>%
add_row(variable = "abundance_hedges_d", axis_name = "Density ES") %>%
add_row(variable = "beta_diversity_from_isolated", axis_name = "Divergence from isolated") %>%
add_row(variable = "beta_diversity_from_previous_time", axis_name = "Temporal Divergence") %>%
add_row(variable = "beta_diversity_from_previous_time_d", axis_name = "Temporal Divergence ES") %>%
add_row(variable = "evenness_pielou", axis_name = "Evenness") %>%
add_row(variable = "evenness_pielou_d", axis_name = "Evenness ES") %>%
slice(-1)
parameters_treatments = data.frame(treatment = NA,
colour = NA,
linetype = NA) %>%
add_row(treatment = "Small isolated", colour = "#bdbdbd", linetype = "solid") %>%
add_row(treatment = "Small connected to small", colour = "#feb24c", linetype = "dashed") %>%
add_row(treatment = "Small connected to large", colour = "#feb24c", linetype = "solid") %>%
add_row(treatment = "Medium isolated", colour = "#737373", linetype = "solid") %>%
add_row(treatment = "Medium connected to medium", colour = "#de2d26", linetype = "solid") %>%
add_row(treatment = "Large isolated", colour = "#000000", linetype = "solid") %>%
add_row(treatment = "Large connected to large", colour = "#3182bd", linetype = "dashed") %>%
add_row(treatment = "Large connected to small", colour = "#3182bd", linetype = "solid") %>%
add_row(treatment = "Small-Small meta-ecosystem", colour = "#fc9272", linetype = "solid") %>%
add_row(treatment = "Medium-Medium meta-ecosystem", colour = "#1b7837", linetype = "solid") %>%
add_row(treatment = "Medium-Medium isolated", colour = "#1b7837", linetype = "dashed") %>%
add_row(treatment = "Large-Large meta-ecosystem", colour = "#67000d", linetype = "solid") %>%
add_row(treatment = "Small-Large meta-ecosystem", colour = "#762a83", linetype = "solid") %>%
add_row(treatment = "Small-Large isolated", colour = "#762a83", linetype = "dashed") %>%
slice(-1)
figures_height_rmd_output = 7
legend_position = "top"
legend_width_cm = 2
size_legend = 12
size_x_axis = 13
size_y_axis = size_x_axis
boxplot_width = 2
dodging = 0.5
width_errorbar = 0.2
dodging_error_bar = 0.5
treatment_lines_linewidth = 1
treatment_points_size = 2.5
resource_flow_line_type = "solid"
resource_flow_line_colour = "#d9d9d9"
resource_flow_line_width = 0.3
zero_line_colour = "grey"
zero_line_line_type = "dotted"
zero_line_line_width = 0.5
zero_line_ES_line_type = "dotted"
zero_line_ES_colour = "grey"
zero_line_ES_line_width = 1
ggarrange_margin_top = 0
ggarrange_margin_bottom = 0
ggarrange_margin_left = 0
ggarrange_margin_right = 0
paper_width = 17.3
paper_height = 20
paper_units = "cm"
paper_res = 600
presentation_figure_size = 15
presentation_figure_width = 30
presentation_figure_height = 22
presentation_legend_size = 20
presentation_x_axis_size = 20
presentation_y_axis_size = presentation_x_axis_size
presentation_axes_size = 12
presentation_treatment_points_size = 5
presentation_treatment_linewidth = 2
presentation_figure_units = "cm"
presentation_figure_res = 600# Modelling
first_time_point_model = 2 #Time point 2 is the first after disturbance
last_time_point_model = last_time_point
optimizer_input = 'Nelder_Mead'
method_input = ''
compar_ex = "The full and fixed models are compared to the null model."
results_table = data.frame(
Response = as.character(NA),
Levels = as.character(NA),
ΔAIC_full = NA,
p_full = NA,
ΔR2_full = NA,
ΔAIC_fix = NA,
p_fix = NA,
ΔR2_fix = NA
) %>%
slice(-1)Data
Experimental cultures (culture_info)
This table contains information about the 110 cultures of the experiment.
# Import culture informations
culture_info = read.csv(here("data", "culture_info.csv"), header = TRUE)Individuals (ds_individuals)
In this dataset (ds_individuals) each row represents an
individual at a time point.
# Import the individual data of t0. We considered cultures to be all the same at the beginning (t0). Because of this reason, we filmed only the bottles from which cultures were assembled. Because we want to plot also t0 for the different treatments, we want to assign the video of bottles to all cultures at t0.
ds_individuals_t0_not_elongated = read.csv(here("data", "individuals", "t0.csv")) %>%
mutate(time_point = as.numeric(str_extract(time_point, "\\d+")),
day = 0,
file = as.numeric(str_extract(file, "\\d+")),
video_replicate = file) %>%
select(time_point,
day,
video_replicate,
file,
id,
N_frames,
mean_area)
ds_individuals_t0_elongated = ds_individuals_t0_not_elongated %>%
map_dfr(.x = 1 : nrow(culture_info),
.f = ~ ds_individuals_t0_not_elongated) %>%
arrange(id) %>% #Id refers to an individual
mutate(culture_ID = rep(1 : nrow(culture_info),
times = nrow(ds_individuals_t0_not_elongated))) %>%
select(time_point,
day,
video_replicate,
file,
culture_ID,
id,
N_frames,
mean_area)
expect_equal(nrow(ds_individuals_t0_not_elongated) * nrow(culture_info),
nrow(ds_individuals_t0_elongated))#Import t1-t4
ds_individuals_t1_to_t4 = NULL
for (time_point_i in time_points_without_t0) {
ds_individuals_t1_to_t4[[time_point_i]] = read.csv(here("data",
"individuals",
paste0("t",
time_point_i,
".csv"))) %>%
mutate(time_point = as.numeric(str_extract(time_point, "\\d+")),
day = time_point_day$day[time_point_day$time_point == time_point_i],
file = as.numeric(str_extract(file, "\\d+")),
video_replicate = ceiling(file/n_cultures)) #Until 110 video replicate = 1, then 2
}
ds_individuals_t1_to_t4 = ds_individuals_t1_to_t4 %>%
bind_rows() %>%
select(time_point,
day,
video_replicate,
file,
culture_ID,
id,
N_frames,
mean_area)# Bind t0 with t1-t4
ds_individuals = rbind(ds_individuals_t0_elongated,
ds_individuals_t1_to_t4) %>%
left_join(culture_info,
by = "culture_ID") # Rename and select columns
ds_individuals = ds_individuals %>%
select(
disturbance,
disturbance_volume,
time_point,
day,
video_replicate,
culture_ID,
system_nr,
file,
eco_metaeco_type,
patch_size,
patch_size_volume,
metaecosystem,
metaecosystem_type,
mean_area,
N_frames
) %>%
rename(patch_size_ml = patch_size_volume,
patch_type = eco_metaeco_type,
body_area_µm2 = mean_area)# Rename and reorder levels
ds_individuals <- ds_individuals %>%
mutate(patch_type = case_when(patch_type == "S" ~ "Small isolated",
patch_type == "M" ~ "Medium isolated",
patch_type == "L" ~ "Large isolated",
patch_type == "S (S_S)" ~ "Small connected to small",
patch_type == "S (S_L)" ~ "Small connected to large",
patch_type == "M (M_M)" ~ "Medium connected to medium",
patch_type == "L (S_L)" ~ "Large connected to small",
patch_type == "L (L_L)" ~ "Large connected to large",
TRUE ~ patch_type),
patch_type = factor(patch_type,
levels = patch_types_ordered))
ds_individuals <- ds_individuals %>%
mutate(patch_size = case_when(patch_size == "S" ~ "Small",
patch_size == "M" ~ "Medium",
patch_size == "L" ~ "Large",
TRUE ~ patch_type),
patch_size = factor(patch_size,
levels = "Small",
"Medium",
"Large"))
ds_individuals <- ds_individuals %>%
mutate(size_connected_patch = case_when(patch_type == "Small connected to small" ~ "Small",
patch_type == "Small connected to large" ~ "Large",
patch_type == "Medium connected to medium" ~ "Medium",
patch_type == "Large connected to large" ~ "Large",
patch_type == "Large connected to small" ~ "Small",
TRUE ~ NA_character_))# Take off problematic videos
ds_individuals_before_taking_off_videos = ds_individuals
ds_individuals = ds_individuals %>%
filter(!(time_point %in% videos_to_take_off$time_point & file %in% videos_to_take_off$file))
diff = setdiff(ds_individuals_before_taking_off_videos, ds_individuals)
expect_equal(nrow(videos_to_take_off),
nrow(expand.grid(diff$culture_ID, diff$time_point, diff$file) %>% unique()))# Take off problematic cultures
ds_individuals_before_taking_off_cultures = ds_individuals
ds_individuals = ds_individuals %>%
filter(!culture_ID %in% patches_to_take_off)
expect_equal(setdiff(ds_individuals_before_taking_off_cultures,
ds_individuals) %>%
pull(culture_ID) %>%
unique(),
patches_to_take_off)Patches (ds_patches)
In this dataset (ds_patches) each row represents a patch
at a time point. I use the data from the 40 threshold analysis for Ble,
Cep, Spi and the data from the 13 threshold analysis for all the other
protists (Col, Eup, Lox, Pau, Pca, Spi te, Tet).
# Import & bind t0 datasets.
ds_patches_t0 = read.csv(here("data", "populations", "t0.csv")) %>%
mutate(time_point = as.numeric(str_extract(time_point, "\\d+")),
day = 0,
video_replicate = file) %>%
select(time_point,
day,
video_replicate,
file,
bioarea_per_volume,
indiv_per_volume)
species_ID_13_threshold_t0 = read.csv(here("data", "species_ID_13_threshold", paste0("t0.csv"))) %>%
rename(Ble_indiv_per_volume = Ble,
Cep_indiv_per_volume = Cep,
Col_indiv_per_volume = Col,
Eug_indiv_per_volume = Eug,
Eup_indiv_per_volume = Eup,
Lox_indiv_per_volume = Lox,
Pau_indiv_per_volume = Pau,
Pca_indiv_per_volume = Pca,
Spi_indiv_per_volume = Spi,
Spi_te_indiv_per_volume = Spi_te,
Tet_indiv_per_volume = Tet) %>%
select(file,
all_of(species_IDD_with_13_threshold_indiv_per_volume))
species_ID_40_threshold_t0 = read.csv(here("data", "species_ID_40_threshold", paste0("t0.csv"))) %>%
rename(Ble_indiv_per_volume = Ble,
Cep_indiv_per_volume = Cep,
Col_indiv_per_volume = Col,
Eug_indiv_per_volume = Eug,
Eup_indiv_per_volume = Eup,
Lox_indiv_per_volume = Lox,
Pau_indiv_per_volume = Pau,
Pca_indiv_per_volume = Pca,
Spi_indiv_per_volume = Spi,
Spi_te_indiv_per_volume = Spi_te,
Tet_indiv_per_volume = Tet) %>%
select(file,
all_of(species_IDD_with_40_threshold_indiv_per_volume))
ds_patches_t0 = ds_patches_t0 %>%
left_join(species_ID_13_threshold_t0,
by = "file") %>%
left_join(species_ID_40_threshold_t0,
by = "file") %>%
mutate(file = as.numeric(str_extract(file, "\\d+")))# Elongate t0 dataset.
ds_patches_t0_elongated <- list()
for (video_i in 1 : n_videos_taken_t0) {
single_video = ds_patches_t0 %>%
filter(file == video_i)
ds_patches_t0_elongated[[video_i]] = culture_info %>%
mutate(
time_point = 0,
day = 0,
file = single_video$file,
video_replicate = single_video$video_replicate,
bioarea_per_volume = single_video$bioarea_per_volume,
indiv_per_volume = single_video$indiv_per_volume,
Ble_indiv_per_volume = single_video$Ble_indiv_per_volume,
Cep_indiv_per_volume = single_video$Cep_indiv_per_volume,
Col_indiv_per_volume = single_video$Col_indiv_per_volume,
Eug_indiv_per_volume = single_video$Eug_indiv_per_volume,
Eup_indiv_per_volume = single_video$Eup_indiv_per_volume,
Lox_indiv_per_volume = single_video$Lox_indiv_per_volume,
Pau_indiv_per_volume = single_video$Pau_indiv_per_volume,
Pca_indiv_per_volume = single_video$Pca_indiv_per_volume,
Spi_indiv_per_volume = single_video$Spi_indiv_per_volume,
Spi_te_indiv_per_volume = single_video$Spi_te_indiv_per_volume,
Tet_indiv_per_volume = single_video$Tet_indiv_per_volume)
}
ds_patches_t0_elongated = ds_patches_t0_elongated %>%
bind_rows()# Clean the columns of t0
ds_patches_t0 = ds_patches_t0_elongated %>%
select(
file,
time_point,
day,
culture_ID,
video_replicate,
bioarea_per_volume,
indiv_per_volume,
all_of(protist_species_indiv_per_volume))
expect_equal(nrow(ds_patches_t0), sum(n_videos_taken_t0 * n_cultures))# Import and bind t1-t4
ds_patches_t1_to_t4 = NULL
for (time_point_i in time_points_without_t0) {
species_ID_13_threshold = read.csv(here("data",
"species_ID_13_threshold",
paste0("t", time_point_i, ".csv"))) %>%
rename(Ble_indiv_per_volume = Ble,
Cep_indiv_per_volume = Cep,
Col_indiv_per_volume = Col,
Eug_indiv_per_volume = Eug,
Eup_indiv_per_volume = Eup,
Lox_indiv_per_volume = Lox,
Pau_indiv_per_volume = Pau,
Pca_indiv_per_volume = Pca,
Spi_indiv_per_volume = Spi,
Spi_te_indiv_per_volume = Spi_te,
Tet_indiv_per_volume = Tet) %>%
select(file,
all_of(species_IDD_with_13_threshold_indiv_per_volume))
species_ID_40_threshold = read.csv(here("data",
"species_ID_40_threshold",
paste0("t", time_point_i, ".csv"))) %>%
rename(Ble_indiv_per_volume = Ble,
Cep_indiv_per_volume = Cep,
Col_indiv_per_volume = Col,
Eug_indiv_per_volume = Eug,
Eup_indiv_per_volume = Eup,
Lox_indiv_per_volume = Lox,
Pau_indiv_per_volume = Pau,
Pca_indiv_per_volume = Pca,
Spi_indiv_per_volume = Spi,
Spi_te_indiv_per_volume = Spi_te,
Tet_indiv_per_volume = Tet) %>%
select(file,
all_of(species_IDD_with_40_threshold_indiv_per_volume))
ds_patches_t1_to_t4[[time_point_i]] = read.csv(here("data",
"populations",
paste0("t", time_point_i, ".csv"))) %>%
arrange(file) %>%
mutate(video_replicate = rep(1 : time_point_day$video_replicates[time_point_i+1],
each = n_cultures),
day = time_point_day$day[time_point_day$time_point == time_point_i]) %>%
select(
file,
time_point,
day,
video_replicate,
file,
culture_ID,
bioarea_per_volume,
indiv_per_volume)
ds_patches_t1_to_t4[[time_point_i]] = ds_patches_t1_to_t4[[time_point_i]] %>%
left_join(species_ID_13_threshold,
by = "file") %>%
left_join(species_ID_40_threshold,
by = "file")
}
ds_patches_t1_to_t4 = ds_patches_t1_to_t4 %>%
bind_rows()# Bind t0 with t1-t4
ds_patches = rbind(ds_patches_t0,
ds_patches_t1_to_t4) %>%
left_join(culture_info,
by = "culture_ID")
expect_equal(nrow(ds_patches),
sum(sum(time_point_day$video_replicates) * n_cultures))# Reorder and rename columns
ds_patches = ds_patches %>%
rename(patch_size_ml = patch_size_volume) %>%
select(
file,
time_point,
day,
disturbance,
culture_ID,
system_nr,
eco_metaeco_type,
patch_size,
patch_size_ml,
metaecosystem,
metaecosystem_type,
video_replicate,
bioarea_per_volume,
indiv_per_volume,
all_of(protist_species_indiv_per_volume)
) %>%
rename(patch_type = eco_metaeco_type,
bioarea_µm2_per_μL = bioarea_per_volume) %>%
rename_all( ~ gsub("volume", "μL", .))# Rename and reorder levels
ds_patches <- ds_patches %>%
mutate(
patch_type = case_when(
patch_type == "S" ~ "Small isolated",
patch_type == "M" ~ "Medium isolated",
patch_type == "L" ~ "Large isolated",
patch_type == "S (S_S)" ~ "Small connected to small",
patch_type == "S (S_L)" ~ "Small connected to large",
patch_type == "M (M_M)" ~ "Medium connected to medium",
patch_type == "L (S_L)" ~ "Large connected to small",
patch_type == "L (L_L)" ~ "Large connected to large",
TRUE ~ patch_type
),
patch_type = factor(x = patch_type,
levels = patch_types_ordered),
patch_size = case_when(
patch_size == "S" ~ "Small",
patch_size == "M" ~ "Medium",
patch_size == "L" ~ "Large",
TRUE ~ patch_size
),
metaecosystem_type = case_when(
metaecosystem_type == "S_S" ~ "Small-Small meta-ecosystem",
metaecosystem_type == "M_M" ~ "Medium-Medium meta-ecosystem",
metaecosystem_type == "L_L" ~ "Large-Large meta-ecosystem",
metaecosystem_type == "S_L" ~ "Small-Large meta-ecosystem",
TRUE ~ metaecosystem_type
),
size_connected_patch = case_when(
patch_type == "Small connected to small" ~ "Small",
patch_type == "Small connected to large" ~ "Large",
patch_type == "Medium connected to medium" ~ "Medium",
patch_type == "Large connected to large" ~ "Large",
patch_type == "Large connected to small" ~ "Small",
TRUE ~ NA_character_
),
time_point = as.numeric(str_extract(time_point, "\\d+")),
file = as.numeric(str_extract(file, "\\d+")),
)# Change units of measurments to ml
ds_patches = ds_patches %>%
mutate(bioarea_µm2_per_ml = bioarea_µm2_per_μL * 10^3,
bioarea_mm2_per_ml = bioarea_µm2_per_ml * 10^(-6),
Ble_indiv_per_ml = Ble_indiv_per_μL * 10^3,
Cep_indiv_per_ml = Cep_indiv_per_μL * 10^3,
Col_indiv_per_ml = Col_indiv_per_μL * 10^3,
Eug_indiv_per_ml = Eug_indiv_per_μL * 10^3,
Eup_indiv_per_ml = Eup_indiv_per_μL * 10^3,
Lox_indiv_per_ml = Lox_indiv_per_μL * 10^3,
Pau_indiv_per_ml = Pau_indiv_per_μL * 10^3,
Pca_indiv_per_ml = Pca_indiv_per_μL * 10^3,
Spi_indiv_per_ml = Spi_indiv_per_μL * 10^3,
Spi_te_indiv_per_ml = Spi_te_indiv_per_μL * 10^3,
Tet_indiv_per_ml = Tet_indiv_per_μL * 10^3)# Take off problematic videos
ds_patches_before_taking_off_videos = ds_patches
ds_patches = ds_patches %>%
filter(!(time_point %in% videos_to_take_off$time_point & file %in% videos_to_take_off$file))
diff = setdiff(ds_patches_before_taking_off_videos, ds_patches)
expect_equal(nrow(videos_to_take_off),
nrow(expand.grid(diff$culture_ID, diff$time_point, diff$file) %>% unique()))# Take off problematic cultures
ds_patches_before_taking_off_cultures = ds_patches
ds_patches = ds_patches %>%
filter(!culture_ID %in% patches_to_take_off)
expect_equal(setdiff(ds_patches_before_taking_off_cultures,
ds_patches) %>%
pull(culture_ID) %>%
unique(),
patches_to_take_off)# Average videos
ds_patches = ds_patches %>%
group_by(across(all_of(columns_patches))) %>%
summarise(across(contains("_per_ml"), mean),
across(contains("_tot"), mean)) %>%
ungroup()
expect_equal(nrow(ds_patches),
(n_cultures - length(patches_to_take_off)) * length(time_points))# Add connection and individuals
ds_patches = ds_patches %>%
mutate(indiv_per_ml = !!rlang::parse_expr(paste(protist_species_indiv_per_ml,
collapse = " + ")))
ds_patches = ds_patches %>%
mutate(connection = ifelse(str_detect(patch_type, "isolated"),
"isolated",
"connected"),
connection = factor(x = connection,
levels = c("isolated", "connected")))# Calculate total response variable for the whole patch
ds_patches = ds_patches %>%
mutate(bioarea_tot_mm2 = bioarea_mm2_per_ml * patch_size_ml,
indiv_tot = indiv_per_ml * patch_size_ml,
Ble_tot_indiv = Ble_indiv_per_ml * patch_size_ml,
Cep_tot_indiv = Cep_indiv_per_ml * patch_size_ml,
Col_tot_indiv = Col_indiv_per_ml * patch_size_ml,
Eug_tot_indiv = Eug_indiv_per_ml * patch_size_ml,
Eup_tot_indiv = Eup_indiv_per_ml * patch_size_ml,
Lox_tot_indiv = Lox_indiv_per_ml * patch_size_ml,
Pau_tot_indiv = Pau_indiv_per_ml * patch_size_ml,
Pca_tot_indiv = Pca_indiv_per_ml * patch_size_ml,
Spi_tot_indiv = Spi_indiv_per_ml * patch_size_ml,
Spi_te_tot_indiv = Spi_te_indiv_per_ml * patch_size_ml,
Tet_tot_indiv = Tet_indiv_per_ml * patch_size_ml)# Calculate species dominance
ds_patches = ds_patches %>%
mutate(across(.cols = all_of(protist_species_indiv_per_ml),
.fns = list(dominance = ~ (. / indiv_per_ml) * 100),
.names = "{col}_dominance"))
expect_equal(unique(ds_patches$Ble_indiv_per_ml_dominance[ds_patches$indiv_per_ml == 0]), NaN)
if (FALSE %in% unique((ds_patches$Ble_indiv_per_ml/ds_patches$indiv_per_ml) *100 == ds_patches$Ble_indiv_per_ml_dominance)) stop()# Calculate alpha diversity (Shannon, Simpson, Inverse Simpson, Evenness)
n_rows_ds_patches_before_calculating_alpha = nrow(ds_patches)
ds_patches = calculate.alpha.diversity()
expect_equal(max(ds_patches$species_richness),
length(protist_species))
expect_equal(nrow(ds_patches),
n_rows_ds_patches_before_calculating_alpha)# Calculate median body size
n_rows_ds_patches_before_median_size = nrow(ds_patches)
ds_median_body_size = ds_individuals %>%
group_by(time_point,
culture_ID,
file) %>%
summarise(median_body_area_µm2 = median(body_area_µm2)) %>%
group_by(time_point,
culture_ID) %>%
summarise(median_body_area_µm2 = mean(median_body_area_µm2))
expect_true(nrow(ds_median_body_size) <= nrow(ds_patches)) #Ds median body size could be less because some cultures might be crashed and not have any individual.
ds_patches_before_full_join = ds_patches
ds_patches = full_join(ds_patches, ds_median_body_size)
expect_equal(nrow(ds_patches),
n_rows_ds_patches_before_median_size)Patch effect sizes (ds_patches_effect_size)
In this dataset (ds_patches_effect_size) each row
represents a treatment at a time point. It contains the effect size of
the connection of a patch (connected vs isolated).
# Calculate the mean & sd of response variables for each treatment/control at each time point
ds_patches_effect_size = NULL
variable_nr = 0
for (variable_i in variables_patches) {
variable_nr = variable_nr + 1
ds_patches_effect_size[[variable_nr]] = ds_patches %>%
filter(time_point >= 1,
!is.na(!!sym(variable_i))) %>%
group_by(across(all_of(columns_patches[columns_patches != "culture_ID" &
columns_patches != "system_nr"]))) %>%
summarise(across(all_of(variable_i),
list(mean = mean,
sd = sd)),
sample_size = n()) %>%
rename_with( ~ paste0(variable_i, "_sample_size"),
matches("sample_size"))
}
ds_patches_effect_size <- reduce(ds_patches_effect_size,
full_join,
by = columns_patches[columns_patches != "culture_ID" &
columns_patches != "system_nr"])
expect_equal(nrow(ds_patches_effect_size),
n_patch_types * (n_time_points-1) * n_disturbance_levels)# Calculate the effect size (Hedge's d) for each treatment at each time point
for (variable_i in variables_patches) {
ds_patches_effect_size <- ds_patches_effect_size %>%
mutate(
!!paste0(variable_i, "_d") := NA,
!!paste0(variable_i, "_d_upper") := NA,
!!paste0(variable_i, "_d_lower") := NA
)
}
row_n = 0
for (treatment_input in treatments_and_controls$treatment) {
for (time_point_input in time_points) {
row_n = row_n + 1
control_input = treatments_and_controls$control[
treatments_and_controls$treatment == treatment_input
]
treatment_row = ds_patches_effect_size %>%
filter(patch_type == treatment_input,
time_point == time_point_input)
control_row = ds_patches_effect_size %>%
filter(patch_type == control_input,
time_point == time_point_input)
for (response_variable in variables_patches) {
hedges_d = calculate.hedges_d(
treatment_row[[paste0(response_variable, "_mean")]],
treatment_row[[paste0(response_variable, "_sd")]],
treatment_row[[paste0(response_variable, "_sample_size")]],
control_row[[paste0(response_variable, "_mean")]],
control_row[[paste0(response_variable, "_sd")]],
control_row[[paste0(response_variable, "_sample_size")]]
)
ds_patches_effect_size[[paste0(response_variable, "_d")]][
ds_patches_effect_size$patch_type == treatment_input &
ds_patches_effect_size$time_point == time_point_input] =
hedges_d$d
ds_patches_effect_size[[paste0(response_variable, "_d_upper")]][
ds_patches_effect_size$patch_type == treatment_input &
ds_patches_effect_size$time_point == time_point_input] =
hedges_d$upper_CI
ds_patches_effect_size[[paste0(response_variable, "_d_lower")]][
ds_patches_effect_size$patch_type == treatment_input &
ds_patches_effect_size$time_point == time_point_input] =
hedges_d$lower_CI
}
}
}
expect_equal(nrow(ds_patches_effect_size),
n_patch_types * (n_time_points-1) * n_disturbance_levels)Meta-ecosystems (ds_metaecosystems)
In this dataset (ds_metaecosystems) each row represents
a meta-ecosystem or a two-patch isolated system at a time point.
#Find combinations of patches in isolated two-patch systems.
ID_isolated_S_low = ds_patches %>%
filter(patch_type == "Small isolated",
disturbance == "low") %>%
pull(culture_ID) %>%
unique()
ID_isolated_M_low = ds_patches %>%
filter(patch_type == "Medium isolated",
disturbance == "low") %>%
pull(culture_ID) %>%
unique()
ID_isolated_L_low = ds_patches %>%
filter(patch_type == "Large isolated",
disturbance == "low") %>%
pull(culture_ID) %>%
unique()
ID_isolated_S_high = ds_patches %>%
filter(patch_type == "Small isolated",
disturbance == "high") %>%
pull(culture_ID) %>%
unique()
ID_isolated_M_high = ds_patches %>%
filter(patch_type == "Medium isolated",
disturbance == "high") %>%
pull(culture_ID) %>%
unique()
ID_isolated_L_high = ds_patches %>%
filter(patch_type == "Large isolated",
disturbance == "high") %>%
pull(culture_ID) %>%
unique()# Find combinations of patches to create different two-patch isolated systems
combinations_S_and_L_low = crossing(ID_isolated_S_low,
ID_isolated_L_low) %>%
mutate(disturbance = "low",
metaecosystem_type = "Small-Large isolated") %>%
rename(ID_first_patch = ID_isolated_S_low,
ID_second_patch = ID_isolated_L_low) %>%
select(disturbance,
metaecosystem_type,
ID_first_patch,
ID_second_patch)
combinations_S_and_L_high = crossing(ID_isolated_S_high,
ID_isolated_L_high) %>%
mutate(disturbance = "high",
metaecosystem_type = "Small-Large isolated") %>%
rename(ID_first_patch = ID_isolated_S_high,
ID_second_patch = ID_isolated_L_high) %>%
select(disturbance,
metaecosystem_type,
ID_first_patch,
ID_second_patch)
combinations_M_and_M_low = combinat::combn(ID_isolated_M_low,
m = 2) %>%
t() %>%
as.data.frame() %>%
rename(ID_first_patch = V1,
ID_second_patch = V2) %>%
mutate(disturbance = "low",
metaecosystem_type = "Medium-Medium isolated") %>%
select(disturbance,
metaecosystem_type,
ID_first_patch,
ID_second_patch)
combinations_M_and_M_high = combinat::combn(ID_isolated_M_high,
m = 2) %>%
t() %>%
as.data.frame() %>%
rename(ID_first_patch = V1,
ID_second_patch = V2) %>%
mutate(disturbance = "high",
metaecosystem_type = "Medium-Medium isolated") %>%
select(disturbance,
metaecosystem_type,
ID_first_patch,
ID_second_patch)# Bind combinations
combinations_isolated_systems = rbind(combinations_S_and_L_low,
combinations_S_and_L_high,
combinations_M_and_M_low,
combinations_M_and_M_high) %>%
mutate(system_nr = 1001:(1000 + nrow(.))) %>%
select(system_nr,
disturbance,
metaecosystem_type,
ID_first_patch,
ID_second_patch)# Find combinations of patches that were connected to form meta-ecosystems.
combinations_metaecos = ds_patches %>%
filter(time_point == 0,
metaecosystem == "yes") %>%
select(system_nr,
disturbance,
metaecosystem_type,
culture_ID) %>%
group_by(system_nr,
disturbance,
metaecosystem_type) %>%
summarise(ID_first_patch = (mean(culture_ID) - 0.5),
ID_second_patch = (mean(culture_ID) + 0.5)) %>%
as.data.frame()#Bind isolated two-patch systems and meta-ecosystems
patch_combinations = rbind(combinations_isolated_systems,
combinations_metaecos) %>%
mutate(connection = ifelse(metaecosystem_type %in% c("Medium-Medium isolated",
"Small-Large isolated"),
yes = "isolated",
no = "connected"),
patches_combined = paste0(ID_first_patch, "|", ID_second_patch))
n_patches_combinations = nrow(patch_combinations)#Create sets for SL isolated, where in each set a small and a large patches are paired differently. To do so, I keep the small patches on the same order and perform permutations on large patches.
SL_isolated_sys_sets <- vector("list",
length(disturbance_levels))
for (disturbance_i in 1:length(disturbance_levels)) {
ID_small_patches = ds_patches %>%
filter(disturbance == disturbance_levels[disturbance_i],
patch_type == "Small isolated") %>%
pull(culture_ID) %>%
unique()
ID_large_patches = ds_patches %>%
filter(disturbance == disturbance_levels[disturbance_i],
patch_type == "Large isolated") %>%
pull(culture_ID) %>%
unique()
#Force small and large patches vectors to have the same length
length_difference <- length(ID_small_patches) - length(ID_large_patches)
if (length_difference > 0) {
ID_large_patches = c(ID_large_patches,
rep("Patch taken off",
times = abs(length(ID_small_patches) -
length(ID_large_patches))))
} else if (length_difference < 0) {
ID_small_patches = c(ID_small_patches,
rep("Patch taken off",
times = abs(length(ID_large_patches) -
length(ID_small_patches))))
}
# Create dataframe
permutations_large = permn(ID_large_patches)
SL_isolated_sys_sets[[disturbance_i]] = data.frame(
disturbance = disturbance_levels[disturbance_i],
metaecosystem_type = "Small-Large isolated",
connection = "isolated",
ID_first_patch = rep(ID_small_patches, times = length(permutations_large)),
ID_second_patch = unlist(permutations_large),
set = rep(1 : length(permutations_large), each = length(ID_small_patches)))
expect_equal(nrow(SL_isolated_sys_sets[[disturbance_i]]),
length(ID_small_patches) * length(permutations_large))
SL_isolated_sys_sets[[disturbance_i]] = SL_isolated_sys_sets[[disturbance_i]] %>%
filter(!ID_first_patch == "Patch taken off",
!ID_second_patch == "Patch taken off") %>%
mutate(ID_first_patch = as.double(ID_first_patch),
ID_second_patch = as.double(ID_second_patch)) %>%
full_join(patch_combinations %>%
filter(disturbance == disturbance_levels[disturbance_i],
metaecosystem_type == "Small-Large isolated")) #Add system_nr & patches_combined
}
SL_isolated_sys_sets_before_binding = SL_isolated_sys_sets
SL_isolated_sys_sets = SL_isolated_sys_sets %>%
bind_rows()expect_equal(nrow(SL_isolated_sys_sets),
nrow(SL_isolated_sys_sets_before_binding[[1]]) + nrow(SL_isolated_sys_sets_before_binding[[2]]))
expect_equal(length(SL_isolated_sys_sets %>%
pull(system_nr) %>%
unique()),
length(patch_combinations %>%
filter(metaecosystem_type == "Small-Large isolated") %>%
pull(system_nr) %>%
unique()))#WORK HERE
#Create sets for MM isolated, where in each set two medium patches small are paired differently. To do so, I ...
#Initialise MM_isolated_sets. Assign 10^4 rows to each matrix so that we have enough rows not to run out of them when we try to assign values to them. Assign 4 columns which will include culture_ID of the first system, second culture_ID of the fist system, culture_ID of the second system, and second culture_ID of the second system.
MM_isolated_sets = NULL
for(disturbance_i in 1:length(disturbance_levels)){
MM_isolated_sets[[disturbance_i]] <- matrix(nrow = 10 ^ 4,
ncol = 4)
}
for (disturbance_i in 1:length(disturbance_levels)) {
ID_medium_patches = ds_patches %>%
filter(disturbance == disturbance_levels[disturbance_i],
patch_type == "Medium isolated") %>%
pull(culture_ID) %>%
unique()
MM_isolated_systems = combn(ID_medium_patches,
2) %>%
t()
matrix_row = 0
for (first_system_i in 1:nrow(MM_isolated_systems)) {
#Find culture IDs of the first system (what's the first system?)
first_system = MM_isolated_systems[first_system_i, ]
for (second_system_i in 1:nrow(MM_isolated_systems)) {
#Find culture IDs of the second system (what's the second system?)
second_system = MM_isolated_systems[second_system_i, ]
shared_elements_among_systems = intersect(first_system,
second_system)
if (length(shared_elements_among_systems) == 0) {
matrix_row = matrix_row + 1
#Make first and second system into a set
MM_isolated_sets[[disturbance_i]][matrix_row,] = c(first_system,
second_system)
print(MM_isolated_sets[[disturbance_i]][matrix_row,])
}
}
}
#Tidy the dataset with all the patch combinations
MM_isolated_sets[[disturbance_i]] = MM_isolated_sets[[disturbance_i]] %>%
as.data.frame() %>%
drop_na()
expect_equal(MM_isolated_sets[[disturbance_i]] %>%
filter(V1 == V2 | V1 == V3 | V1 == V4 | V2 == V3 | V2 == V4 | V3 == V4) %>%
nrow(),
0)
#Reorder the dataset with all the patch combinations
MM_isolated_sets_reordered = data.frame(ID_first_patch = NA,
ID_second_patch = NA,
set = NA)
for (set_input in 1:nrow(MM_isolated_sets[[disturbance_i]])) {
MM_isolated_sets_reordered = MM_isolated_sets_reordered %>%
add_row(
ID_first_patch = MM_isolated_sets[[disturbance_i]][set_input, 1],
ID_second_patch = MM_isolated_sets[[disturbance_i]][set_input, 2],
set = set_input
) %>%
add_row(
ID_first_patch = MM_isolated_sets[[disturbance_i]][set_input, 3],
ID_second_patch = MM_isolated_sets[[disturbance_i]][set_input, 4],
set = set_input
)
}
#Add to a list
MM_isolated_sets[[disturbance_i]] = MM_isolated_sets_reordered %>%
drop_na() %>%
mutate(
disturbance = disturbance_levels[disturbance_i],
metaecosystem_type = "Medium-Medium isolated",
connection = "isolated"
)
#Add system nr
ID_combinations_MM_isolated = patch_combinations %>%
filter(disturbance == disturbance_levels[disturbance_i],
metaecosystem_type == "Medium-Medium isolated")
MM_isolated_sets[[disturbance_i]] = full_join(MM_isolated_sets[[disturbance_i]],
ID_combinations_MM_isolated)
}## [1] 6 7 8 9
## [1] 6 7 8 10
## [1] 6 7 9 10
## [1] 6 8 7 9
## [1] 6 8 7 10
## [1] 6 8 9 10
## [1] 6 9 7 8
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## [1] 7 9 8 10
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## [1] 8 9 6 7
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## [1] 8 10 6 7
## [1] 8 10 6 9
## [1] 8 10 7 9
## [1] 9 10 6 7
## [1] 9 10 6 8
## [1] 9 10 7 8
## [1] 61 62 63 64
## [1] 61 62 63 65
## [1] 61 62 64 65
## [1] 61 63 62 64
## [1] 61 63 62 65
## [1] 61 63 64 65
## [1] 61 64 62 63
## [1] 61 64 62 65
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## [1] 61 65 62 63
## [1] 61 65 62 64
## [1] 61 65 63 64
## [1] 62 63 61 64
## [1] 62 63 61 65
## [1] 62 63 64 65
## [1] 62 64 61 63
## [1] 62 64 61 65
## [1] 62 64 63 65
## [1] 62 65 61 63
## [1] 62 65 61 64
## [1] 62 65 63 64
## [1] 63 64 61 62
## [1] 63 64 61 65
## [1] 63 64 62 65
## [1] 63 65 61 62
## [1] 63 65 61 64
## [1] 63 65 62 64
## [1] 64 65 61 62
## [1] 64 65 61 63
## [1] 64 65 62 63#Bind all sets of MM isolated
MM_isolated_sets = MM_isolated_sets %>%
bind_rows()expect_equal(length(MM_isolated_sets %>%
pull(system_nr) %>%
unique()),
length(patch_combinations %>%
filter(metaecosystem_type == "Medium-Medium isolated") %>%
pull(system_nr) %>%
unique()))#Bind SL and MM isolated systems
isolated_combinations_sets = rbind(SL_isolated_sys_sets,
MM_isolated_sets) %>%
select(disturbance,
metaecosystem_type,
connection,
set,
system_nr,
ID_first_patch,
ID_second_patch)Each row is a meta-ecosystem.
It contains also “fake” meta-ecosystems which I created from isolated patches (
metaecosystem type = Small-Large isolated&metaecosystem type = Medium-Medium isolated).Warning appear after the following code, as:
- alpha diversity is computed for a culture that is totally crashed, so it has not meaning.
- beta diversity is computed between a culture that is still alive and one that is totally crashed. Because one of the cultures is totally crashed, the Bray-Curtis index is 1.
#Compute meta-ecosystems for each time point
ds_metaecosystems = NULL
row_n = 0
for (combination in 1:n_patches_combinations) {
for (time_point_input in time_points) {
row_n = row_n + 1
current_day = sampling_days[time_point_input + 1]
current_system_nr = patch_combinations[combination, ]$system_nr
current_combination = patch_combinations[combination, ]$patches_combined
current_disturbance = patch_combinations[combination, ]$disturbance
current_metaeco_type = patch_combinations[combination, ]$metaecosystem_type
current_IDs = c(patch_combinations[combination, ]$ID_first_patch,
patch_combinations[combination, ]$ID_second_patch)
if (current_system_nr %in% metaecosystems_to_take_off)
next
if (current_IDs[1] == current_IDs[2])
next
species_vector_two_patches = ds_patches %>%
filter(time_point == time_point_input,
culture_ID %in% current_IDs) %>%
ungroup() %>%
select(all_of(protist_species_indiv_per_ml))
absence_presence_two_patches <-
ifelse(species_vector_two_patches > 0, 1, 0)
#Alpha diversity: Shannon (mean between the two patches)
shannon_patch_1 = diversity(species_vector_two_patches[1, ], index = "shannon")
shannon_patch_2 = diversity(species_vector_two_patches[2, ], index = "shannon")
shannon_value = (shannon_patch_1 + shannon_patch_2) / 2
#Alpha diversity: Species richness (mean between the two patches)
richness_patch_1 = specnumber(species_vector_two_patches[1, ])
richness_patch_2 = specnumber(species_vector_two_patches[2, ])
mean_richness_value = (richness_patch_1 + richness_patch_2) / 2
#Beta diversity: Jaccard
jaccard_index_value = vegdist(species_vector_two_patches,
method = "jaccard") %>%
as.numeric()
#Beta diversity: Bray Curtis
bray_curtis_value = vegdist(species_vector_two_patches,
method = "bray") %>%
as.numeric()
#Beta diversity: partitioning of beta diversity from Sorensen index into turnover (Simpson pair-wise dissimilarity) and nestedness (nestedness-fraction of Sorensen)
betapart_core_object = betapart.core(absence_presence_two_patches)
beta_spatial_turnover_value = beta.pair(betapart_core_object)$beta.sim %>% as.double()
beta_nestedness_value = beta.pair(betapart_core_object)$beta.sne %>% as.double()
beta_total_value = beta.pair(betapart_core_object)$beta.sor %>% as.double()
#Gamma diversity: Meta-ecosystem richness
metaecosystem_richness_value = colSums(species_vector_two_patches) %>%
specnumber()
#Put everything together
ds_metaecosystems[[row_n]] = ds_patches %>%
filter(culture_ID %in% current_IDs,
time_point == time_point_input) %>%
summarise(
total_metaecosystem_bioarea_mm2 = sum(bioarea_tot_mm2),
total_metaecosystem_Ble_indiv = sum(Ble_tot_indiv),
total_metaecosystem_Cep_indiv = sum(Cep_tot_indiv),
total_metaecosystem_Col_indiv = sum(Col_tot_indiv),
total_metaecosystem_Eug_indiv = sum(Eug_tot_indiv),
total_metaecosystem_Eup_indiv = sum(Eup_tot_indiv),
total_metaecosystem_Lox_indiv = sum(Lox_tot_indiv),
total_metaecosystem_Pau_indiv = sum(Pau_tot_indiv),
total_metaecosystem_Pca_indiv = sum(Pca_tot_indiv),
total_metaecosystem_Spi_indiv = sum(Spi_tot_indiv),
total_metaecosystem_Spi_te_indiv = sum(Spi_te_tot_indiv),
total_metaecosystem_Tet_indiv = sum(Tet_tot_indiv)
) %>%
mutate(
system_nr = current_system_nr,
patches_combined = current_combination,
metaecosystem_type = current_metaeco_type,
disturbance = current_disturbance,
time_point = time_point_input,
day = current_day,
jaccard_index = jaccard_index_value,
bray_curtis = bray_curtis_value,
beta_spatial_turnover = beta_spatial_turnover_value,
beta_nestedness = beta_nestedness_value,
beta_total = beta_total_value,
metaecosystem_richness = metaecosystem_richness_value,
mean_shannon = shannon_value,
mean_richness = mean_richness_value
) %>%
ungroup()
}
}## Warning in vegdist(species_vector_two_patches, method = "jaccard"): you have empty rows: their dissimilarities may be
## meaningless in method "jaccard"## Warning in vegdist(species_vector_two_patches, method = "bray"): you have empty rows: their dissimilarities may be
## meaningless in method "bray"## Warning in vegdist(species_vector_two_patches, method = "jaccard"): you have empty rows: their dissimilarities may be
## meaningless in method "jaccard"## Warning in vegdist(species_vector_two_patches, method = "bray"): you have empty rows: their dissimilarities may be
## meaningless in method "bray"## Warning in vegdist(species_vector_two_patches, method = "jaccard"): you have empty rows: their dissimilarities may be
## meaningless in method "jaccard"## Warning in vegdist(species_vector_two_patches, method = "bray"): you have empty rows: their dissimilarities may be
## meaningless in method "bray"## Warning in vegdist(species_vector_two_patches, method = "jaccard"): you have empty rows: their dissimilarities may be
## meaningless in method "jaccard"## Warning in vegdist(species_vector_two_patches, method = "bray"): you have empty rows: their dissimilarities may be
## meaningless in method "bray"## Warning in vegdist(species_vector_two_patches, method = "jaccard"): you have empty rows: their dissimilarities may be
## meaningless in method "jaccard"## Warning in vegdist(species_vector_two_patches, method = "bray"): you have empty rows: their dissimilarities may be
## meaningless in method "bray"## Warning in vegdist(species_vector_two_patches, method = "jaccard"): you have empty rows: their dissimilarities may be
## meaningless in method "jaccard"## Warning in vegdist(species_vector_two_patches, method = "bray"): you have empty rows: their dissimilarities may be
## meaningless in method "bray"## Warning in vegdist(species_vector_two_patches, method = "jaccard"): you have empty rows: their dissimilarities may be
## meaningless in method "jaccard"## Warning in vegdist(species_vector_two_patches, method = "bray"): you have empty rows: their dissimilarities may be
## meaningless in method "bray"## Warning in vegdist(species_vector_two_patches, method = "jaccard"): you have empty rows: their dissimilarities may be
## meaningless in method "jaccard"## Warning in vegdist(species_vector_two_patches, method = "bray"): you have empty rows: their dissimilarities may be
## meaningless in method "bray"## Warning in vegdist(species_vector_two_patches, method = "jaccard"): you have empty rows: their dissimilarities may be
## meaningless in method "jaccard"## Warning in vegdist(species_vector_two_patches, method = "bray"): you have empty rows: their dissimilarities may be
## meaningless in method "bray"## Warning in vegdist(species_vector_two_patches, method = "jaccard"): you have empty rows: their dissimilarities may be
## meaningless in method "jaccard"## Warning in vegdist(species_vector_two_patches, method = "bray"): you have empty rows: their dissimilarities may be
## meaningless in method "bray"## Warning in vegdist(species_vector_two_patches, method = "jaccard"): you have empty rows: their dissimilarities may be
## meaningless in method "jaccard"## Warning in vegdist(species_vector_two_patches, method = "bray"): you have empty rows: their dissimilarities may be
## meaningless in method "bray"## Warning in vegdist(species_vector_two_patches, method = "jaccard"): you have empty rows: their dissimilarities may be
## meaningless in method "jaccard"## Warning in vegdist(species_vector_two_patches, method = "bray"): you have empty rows: their dissimilarities may be
## meaningless in method "bray"## Warning in vegdist(species_vector_two_patches, method = "jaccard"): you have empty rows: their dissimilarities may be
## meaningless in method "jaccard"## Warning in vegdist(species_vector_two_patches, method = "bray"): you have empty rows: their dissimilarities may be
## meaningless in method "bray"## Warning in vegdist(species_vector_two_patches, method = "jaccard"): you have empty rows: their dissimilarities may be
## meaningless in method "jaccard"## Warning in vegdist(species_vector_two_patches, method = "bray"): you have empty rows: their dissimilarities may be
## meaningless in method "bray"## Warning in vegdist(species_vector_two_patches, method = "jaccard"): you have empty rows: their dissimilarities may be
## meaningless in method "jaccard"## Warning in vegdist(species_vector_two_patches, method = "bray"): you have empty rows: their dissimilarities may be
## meaningless in method "bray"## Warning in vegdist(species_vector_two_patches, method = "jaccard"): you have empty rows: their dissimilarities may be
## meaningless in method "jaccard"## Warning in vegdist(species_vector_two_patches, method = "bray"): you have empty rows: their dissimilarities may be
## meaningless in method "bray"## Warning in vegdist(species_vector_two_patches, method = "jaccard"): you have empty rows: their dissimilarities may be
## meaningless in method "jaccard"## Warning in vegdist(species_vector_two_patches, method = "bray"): you have empty rows: their dissimilarities may be
## meaningless in method "bray"## Warning in vegdist(species_vector_two_patches, method = "jaccard"): you have empty rows: their dissimilarities may be
## meaningless in method "jaccard"## Warning in vegdist(species_vector_two_patches, method = "bray"): you have empty rows: their dissimilarities may be
## meaningless in method "bray"## Warning in vegdist(species_vector_two_patches, method = "jaccard"): you have empty rows: their dissimilarities may be
## meaningless in method "jaccard"## Warning in vegdist(species_vector_two_patches, method = "bray"): you have empty rows: their dissimilarities may be
## meaningless in method "bray"## Warning in vegdist(species_vector_two_patches, method = "jaccard"): you have empty rows: their dissimilarities may be
## meaningless in method "jaccard"## Warning in vegdist(species_vector_two_patches, method = "bray"): you have empty rows: their dissimilarities may be
## meaningless in method "bray"## Warning in vegdist(species_vector_two_patches, method = "jaccard"): you have empty rows: their dissimilarities may be
## meaningless in method "jaccard"## Warning in vegdist(species_vector_two_patches, method = "bray"): you have empty rows: their dissimilarities may be
## meaningless in method "bray"## Warning in vegdist(species_vector_two_patches, method = "jaccard"): you have empty rows: their dissimilarities may be
## meaningless in method "jaccard"## Warning in vegdist(species_vector_two_patches, method = "bray"): you have empty rows: their dissimilarities may be
## meaningless in method "bray"## Warning in vegdist(species_vector_two_patches, method = "jaccard"): you have empty rows: their dissimilarities may be
## meaningless in method "jaccard"## Warning in vegdist(species_vector_two_patches, method = "bray"): you have empty rows: their dissimilarities may be
## meaningless in method "bray"## Warning in vegdist(species_vector_two_patches, method = "jaccard"): you have empty rows: their dissimilarities may be
## meaningless in method "jaccard"## Warning in vegdist(species_vector_two_patches, method = "bray"): you have empty rows: their dissimilarities may be
## meaningless in method "bray"## Warning in vegdist(species_vector_two_patches, method = "jaccard"): you have empty rows: their dissimilarities may be
## meaningless in method "jaccard"## Warning in vegdist(species_vector_two_patches, method = "bray"): you have empty rows: their dissimilarities may be
## meaningless in method "bray"## Warning in vegdist(species_vector_two_patches, method = "jaccard"): you have empty rows: their dissimilarities may be
## meaningless in method "jaccard"## Warning in vegdist(species_vector_two_patches, method = "bray"): you have empty rows: their dissimilarities may be
## meaningless in method "bray"## Warning in vegdist(species_vector_two_patches, method = "jaccard"): you have empty rows: their dissimilarities may be
## meaningless in method "jaccard"## Warning in vegdist(species_vector_two_patches, method = "bray"): you have empty rows: their dissimilarities may be
## meaningless in method "bray"## Warning in vegdist(species_vector_two_patches, method = "jaccard"): you have empty rows: their dissimilarities may be
## meaningless in method "jaccard"## Warning in vegdist(species_vector_two_patches, method = "bray"): you have empty rows: their dissimilarities may be
## meaningless in method "bray"## Warning in vegdist(species_vector_two_patches, method = "jaccard"): you have empty rows: their dissimilarities may be
## meaningless in method "jaccard"## Warning in vegdist(species_vector_two_patches, method = "bray"): you have empty rows: their dissimilarities may be
## meaningless in method "bray"## Warning in vegdist(species_vector_two_patches, method = "jaccard"): you have empty rows: their dissimilarities may be
## meaningless in method "jaccard"## Warning in vegdist(species_vector_two_patches, method = "bray"): you have empty rows: their dissimilarities may be
## meaningless in method "bray"## Warning in vegdist(species_vector_two_patches, method = "jaccard"): you have empty rows: their dissimilarities may be
## meaningless in method "jaccard"## Warning in vegdist(species_vector_two_patches, method = "bray"): you have empty rows: their dissimilarities may be
## meaningless in method "bray"## Warning in vegdist(species_vector_two_patches, method = "jaccard"): you have empty rows: their dissimilarities may be
## meaningless in method "jaccard"## Warning in vegdist(species_vector_two_patches, method = "bray"): you have empty rows: their dissimilarities may be
## meaningless in method "bray"## Warning in vegdist(species_vector_two_patches, method = "jaccard"): you have empty rows: their dissimilarities may be
## meaningless in method "jaccard"## Warning in vegdist(species_vector_two_patches, method = "bray"): you have empty rows: their dissimilarities may be
## meaningless in method "bray"## Warning in vegdist(species_vector_two_patches, method = "jaccard"): you have empty rows: their dissimilarities may be
## meaningless in method "jaccard"## Warning in vegdist(species_vector_two_patches, method = "bray"): you have empty rows: their dissimilarities may be
## meaningless in method "bray"## Warning in vegdist(species_vector_two_patches, method = "jaccard"): you have empty rows: their dissimilarities may be
## meaningless in method "jaccard"## Warning in vegdist(species_vector_two_patches, method = "bray"): you have empty rows: their dissimilarities may be
## meaningless in method "bray"## Warning in vegdist(species_vector_two_patches, method = "jaccard"): you have empty rows: their dissimilarities may be
## meaningless in method "jaccard"## Warning in vegdist(species_vector_two_patches, method = "jaccard"): missing
## values in results## Warning in vegdist(species_vector_two_patches, method = "bray"): you have empty rows: their dissimilarities may be
## meaningless in method "bray"## Warning in vegdist(species_vector_two_patches, method = "bray"): missing values
## in results## Warning in vegdist(species_vector_two_patches, method = "jaccard"): you have empty rows: their dissimilarities may be
## meaningless in method "jaccard"## Warning in vegdist(species_vector_two_patches, method = "jaccard"): missing
## values in results## Warning in vegdist(species_vector_two_patches, method = "bray"): you have empty rows: their dissimilarities may be
## meaningless in method "bray"## Warning in vegdist(species_vector_two_patches, method = "bray"): missing values
## in results## Warning in vegdist(species_vector_two_patches, method = "jaccard"): you have empty rows: their dissimilarities may be
## meaningless in method "jaccard"## Warning in vegdist(species_vector_two_patches, method = "bray"): you have empty rows: their dissimilarities may be
## meaningless in method "bray"## Warning in vegdist(species_vector_two_patches, method = "jaccard"): you have empty rows: their dissimilarities may be
## meaningless in method "jaccard"## Warning in vegdist(species_vector_two_patches, method = "bray"): you have empty rows: their dissimilarities may be
## meaningless in method "bray"ds_metaecosystems = ds_metaecosystems %>%
bind_rows() %>%
as.data.frame() %>%
select(
time_point,
day,
system_nr,
patches_combined,
disturbance,
metaecosystem_type,
mean_shannon,
mean_richness,
jaccard_index,
bray_curtis,
beta_spatial_turnover,
beta_nestedness,
beta_total,
metaecosystem_richness,
total_metaecosystem_bioarea_mm2,
paste0("total_metaecosystem_", protist_species, "_indiv")
)expect_equal(nrow(ds_metaecosystems),
n_time_points * n_patches_combinations)#Add column connection
ds_metaecosystems$connection <-
ifelse(
ds_metaecosystems$metaecosystem_type %in% c("Medium-Medium isolated",
"Small-Large isolated"),
yes = "isolated",
no = "connected"
)#Add column patch size symmetry
ds_metaecosystems$patch_size_symmetry <-
ifelse(
ds_metaecosystems$metaecosystem_type %in% c("Small-Large isolated",
"Small-Large meta-ecosystem"),
yes = "asymmetric",
no = "symmetric"
)#Reorder the meta-ecosystems
ds_metaecosystems = ds_metaecosystems %>%
mutate(metaecosystem_type = factor(metaecosystem_type,
levels = metaecosystem_types_ordered))#Join ...
full_join(ds_metaecosystems,
isolated_combinations_sets)## Warning in full_join(ds_metaecosystems, isolated_combinations_sets): Detected an unexpected many-to-many relationship between `x` and `y`.
## ℹ Row 1 of `x` matches multiple rows in `y`.
## ℹ Row 1 of `y` matches multiple rows in `x`.
## ℹ If a many-to-many relationship is expected, set `relationship =
## "many-to-many"` to silence this warning.## time_point day system_nr patches_combined disturbance metaecosystem_type
## 1 0 0 1001 1|11 low Small-Large isolated
## 2 0 0 1001 1|11 low Small-Large isolated
## 3 0 0 1001 1|11 low Small-Large isolated
## 4 0 0 1001 1|11 low Small-Large isolated
## 5 0 0 1001 1|11 low Small-Large isolated
## 6 0 0 1001 1|11 low Small-Large isolated
## 7 0 0 1001 1|11 low Small-Large isolated
## 8 0 0 1001 1|11 low Small-Large isolated
## 9 0 0 1001 1|11 low Small-Large isolated
## 10 0 0 1001 1|11 low Small-Large isolated
## 11 0 0 1001 1|11 low Small-Large isolated
## 12 0 0 1001 1|11 low Small-Large isolated
## 13 0 0 1001 1|11 low Small-Large isolated
## 14 0 0 1001 1|11 low Small-Large isolated
## 15 0 0 1001 1|11 low Small-Large isolated
## 16 0 0 1001 1|11 low Small-Large isolated
## 17 0 0 1001 1|11 low Small-Large isolated
## 18 0 0 1001 1|11 low Small-Large isolated
## 19 0 0 1001 1|11 low Small-Large isolated
## 20 0 0 1001 1|11 low Small-Large isolated
## 21 0 0 1001 1|11 low Small-Large isolated
## 22 0 0 1001 1|11 low Small-Large isolated
## 23 0 0 1001 1|11 low Small-Large isolated
## 24 0 0 1001 1|11 low Small-Large isolated
## 25 1 4 1001 1|11 low Small-Large isolated
## 26 1 4 1001 1|11 low Small-Large isolated
## 27 1 4 1001 1|11 low Small-Large isolated
## 28 1 4 1001 1|11 low Small-Large isolated
## 29 1 4 1001 1|11 low Small-Large isolated
## 30 1 4 1001 1|11 low Small-Large isolated
## 31 1 4 1001 1|11 low Small-Large isolated
## 32 1 4 1001 1|11 low Small-Large isolated
## 33 1 4 1001 1|11 low Small-Large isolated
## 34 1 4 1001 1|11 low Small-Large isolated
## 35 1 4 1001 1|11 low Small-Large isolated
## 36 1 4 1001 1|11 low Small-Large isolated
## 37 1 4 1001 1|11 low Small-Large isolated
## 38 1 4 1001 1|11 low Small-Large isolated
## 39 1 4 1001 1|11 low Small-Large isolated
## 40 1 4 1001 1|11 low Small-Large isolated
## 41 1 4 1001 1|11 low Small-Large isolated
## 42 1 4 1001 1|11 low Small-Large isolated
## 43 1 4 1001 1|11 low Small-Large isolated
## 44 1 4 1001 1|11 low Small-Large isolated
## 45 1 4 1001 1|11 low Small-Large isolated
## 46 1 4 1001 1|11 low Small-Large isolated
## 47 1 4 1001 1|11 low Small-Large isolated
## 48 1 4 1001 1|11 low Small-Large isolated
## 49 2 8 1001 1|11 low Small-Large isolated
## 50 2 8 1001 1|11 low Small-Large isolated
## 51 2 8 1001 1|11 low Small-Large isolated
## 52 2 8 1001 1|11 low Small-Large isolated
## 53 2 8 1001 1|11 low Small-Large isolated
## 54 2 8 1001 1|11 low Small-Large isolated
## 55 2 8 1001 1|11 low Small-Large isolated
## 56 2 8 1001 1|11 low Small-Large isolated
## 57 2 8 1001 1|11 low Small-Large isolated
## 58 2 8 1001 1|11 low Small-Large isolated
## 59 2 8 1001 1|11 low Small-Large isolated
## 60 2 8 1001 1|11 low Small-Large isolated
## 61 2 8 1001 1|11 low Small-Large isolated
## 62 2 8 1001 1|11 low Small-Large isolated
## 63 2 8 1001 1|11 low Small-Large isolated
## 64 2 8 1001 1|11 low Small-Large isolated
## 65 2 8 1001 1|11 low Small-Large isolated
## 66 2 8 1001 1|11 low Small-Large isolated
## 67 2 8 1001 1|11 low Small-Large isolated
## 68 2 8 1001 1|11 low Small-Large isolated
## 69 2 8 1001 1|11 low Small-Large isolated
## 70 2 8 1001 1|11 low Small-Large isolated
## 71 2 8 1001 1|11 low Small-Large isolated
## 72 2 8 1001 1|11 low Small-Large isolated
## 73 3 12 1001 1|11 low Small-Large isolated
## 74 3 12 1001 1|11 low Small-Large isolated
## 75 3 12 1001 1|11 low Small-Large isolated
## 76 3 12 1001 1|11 low Small-Large isolated
## 77 3 12 1001 1|11 low Small-Large isolated
## 78 3 12 1001 1|11 low Small-Large isolated
## 79 3 12 1001 1|11 low Small-Large isolated
## 80 3 12 1001 1|11 low Small-Large isolated
## 81 3 12 1001 1|11 low Small-Large isolated
## 82 3 12 1001 1|11 low Small-Large isolated
## 83 3 12 1001 1|11 low Small-Large isolated
## 84 3 12 1001 1|11 low Small-Large isolated
## 85 3 12 1001 1|11 low Small-Large isolated
## 86 3 12 1001 1|11 low Small-Large isolated
## 87 3 12 1001 1|11 low Small-Large isolated
## 88 3 12 1001 1|11 low Small-Large isolated
## 89 3 12 1001 1|11 low Small-Large isolated
## 90 3 12 1001 1|11 low Small-Large isolated
## 91 3 12 1001 1|11 low Small-Large isolated
## 92 3 12 1001 1|11 low Small-Large isolated
## 93 3 12 1001 1|11 low Small-Large isolated
## 94 3 12 1001 1|11 low Small-Large isolated
## 95 3 12 1001 1|11 low Small-Large isolated
## 96 3 12 1001 1|11 low Small-Large isolated
## 97 4 16 1001 1|11 low Small-Large isolated
## 98 4 16 1001 1|11 low Small-Large isolated
## 99 4 16 1001 1|11 low Small-Large isolated
## 100 4 16 1001 1|11 low Small-Large isolated
## 101 4 16 1001 1|11 low Small-Large isolated
## 102 4 16 1001 1|11 low Small-Large isolated
## 103 4 16 1001 1|11 low Small-Large isolated
## 104 4 16 1001 1|11 low Small-Large isolated
## 105 4 16 1001 1|11 low Small-Large isolated
## 106 4 16 1001 1|11 low Small-Large isolated
## 107 4 16 1001 1|11 low Small-Large isolated
## 108 4 16 1001 1|11 low Small-Large isolated
## 109 4 16 1001 1|11 low Small-Large isolated
## 110 4 16 1001 1|11 low Small-Large isolated
## 111 4 16 1001 1|11 low Small-Large isolated
## 112 4 16 1001 1|11 low Small-Large isolated
## 113 4 16 1001 1|11 low Small-Large isolated
## 114 4 16 1001 1|11 low Small-Large isolated
## 115 4 16 1001 1|11 low Small-Large isolated
## 116 4 16 1001 1|11 low Small-Large isolated
## 117 4 16 1001 1|11 low Small-Large isolated
## 118 4 16 1001 1|11 low Small-Large isolated
## 119 4 16 1001 1|11 low Small-Large isolated
## 120 4 16 1001 1|11 low Small-Large isolated
## 121 5 20 1001 1|11 low Small-Large isolated
## 122 5 20 1001 1|11 low Small-Large isolated
## 123 5 20 1001 1|11 low Small-Large isolated
## 124 5 20 1001 1|11 low Small-Large isolated
## 125 5 20 1001 1|11 low Small-Large isolated
## 126 5 20 1001 1|11 low Small-Large isolated
## 127 5 20 1001 1|11 low Small-Large isolated
## 128 5 20 1001 1|11 low Small-Large isolated
## 129 5 20 1001 1|11 low Small-Large isolated
## 130 5 20 1001 1|11 low Small-Large isolated
## 131 5 20 1001 1|11 low Small-Large isolated
## 132 5 20 1001 1|11 low Small-Large isolated
## 133 5 20 1001 1|11 low Small-Large isolated
## 134 5 20 1001 1|11 low Small-Large isolated
## 135 5 20 1001 1|11 low Small-Large isolated
## 136 5 20 1001 1|11 low Small-Large isolated
## 137 5 20 1001 1|11 low Small-Large isolated
## 138 5 20 1001 1|11 low Small-Large isolated
## 139 5 20 1001 1|11 low Small-Large isolated
## 140 5 20 1001 1|11 low Small-Large isolated
## 141 5 20 1001 1|11 low Small-Large isolated
## 142 5 20 1001 1|11 low Small-Large isolated
## 143 5 20 1001 1|11 low Small-Large isolated
## 144 5 20 1001 1|11 low Small-Large isolated
## 145 6 24 1001 1|11 low Small-Large isolated
## 146 6 24 1001 1|11 low Small-Large isolated
## 147 6 24 1001 1|11 low Small-Large isolated
## 148 6 24 1001 1|11 low Small-Large isolated
## 149 6 24 1001 1|11 low Small-Large isolated
## 150 6 24 1001 1|11 low Small-Large isolated
## 151 6 24 1001 1|11 low Small-Large isolated
## 152 6 24 1001 1|11 low Small-Large isolated
## 153 6 24 1001 1|11 low Small-Large isolated
## 154 6 24 1001 1|11 low Small-Large isolated
## 155 6 24 1001 1|11 low Small-Large isolated
## 156 6 24 1001 1|11 low Small-Large isolated
## 157 6 24 1001 1|11 low Small-Large isolated
## 158 6 24 1001 1|11 low Small-Large isolated
## 159 6 24 1001 1|11 low Small-Large isolated
## 160 6 24 1001 1|11 low Small-Large isolated
## 161 6 24 1001 1|11 low Small-Large isolated
## 162 6 24 1001 1|11 low Small-Large isolated
## 163 6 24 1001 1|11 low Small-Large isolated
## 164 6 24 1001 1|11 low Small-Large isolated
## 165 6 24 1001 1|11 low Small-Large isolated
## 166 6 24 1001 1|11 low Small-Large isolated
## 167 6 24 1001 1|11 low Small-Large isolated
## 168 6 24 1001 1|11 low Small-Large isolated
## 169 7 28 1001 1|11 low Small-Large isolated
## 170 7 28 1001 1|11 low Small-Large isolated
## 171 7 28 1001 1|11 low Small-Large isolated
## 172 7 28 1001 1|11 low Small-Large isolated
## 173 7 28 1001 1|11 low Small-Large isolated
## 174 7 28 1001 1|11 low Small-Large isolated
## 175 7 28 1001 1|11 low Small-Large isolated
## 176 7 28 1001 1|11 low Small-Large isolated
## 177 7 28 1001 1|11 low Small-Large isolated
## 178 7 28 1001 1|11 low Small-Large isolated
## 179 7 28 1001 1|11 low Small-Large isolated
## 180 7 28 1001 1|11 low Small-Large isolated
## 181 7 28 1001 1|11 low Small-Large isolated
## 182 7 28 1001 1|11 low Small-Large isolated
## 183 7 28 1001 1|11 low Small-Large isolated
## 184 7 28 1001 1|11 low Small-Large isolated
## 185 7 28 1001 1|11 low Small-Large isolated
## 186 7 28 1001 1|11 low Small-Large isolated
## 187 7 28 1001 1|11 low Small-Large isolated
## 188 7 28 1001 1|11 low Small-Large isolated
## 189 7 28 1001 1|11 low Small-Large isolated
## 190 7 28 1001 1|11 low Small-Large isolated
## 191 7 28 1001 1|11 low Small-Large isolated
## 192 7 28 1001 1|11 low Small-Large isolated
## 193 0 0 1002 1|12 low Small-Large isolated
## 194 0 0 1002 1|12 low Small-Large isolated
## 195 0 0 1002 1|12 low Small-Large isolated
## 196 0 0 1002 1|12 low Small-Large isolated
## 197 0 0 1002 1|12 low Small-Large isolated
## 198 0 0 1002 1|12 low Small-Large isolated
## 199 0 0 1002 1|12 low Small-Large isolated
## 200 0 0 1002 1|12 low Small-Large isolated
## 201 0 0 1002 1|12 low Small-Large isolated
## 202 0 0 1002 1|12 low Small-Large isolated
## 203 0 0 1002 1|12 low Small-Large isolated
## 204 0 0 1002 1|12 low Small-Large isolated
## 205 0 0 1002 1|12 low Small-Large isolated
## 206 0 0 1002 1|12 low Small-Large isolated
## 207 0 0 1002 1|12 low Small-Large isolated
## 208 0 0 1002 1|12 low Small-Large isolated
## 209 0 0 1002 1|12 low Small-Large isolated
## 210 0 0 1002 1|12 low Small-Large isolated
## 211 0 0 1002 1|12 low Small-Large isolated
## 212 0 0 1002 1|12 low Small-Large isolated
## 213 0 0 1002 1|12 low Small-Large isolated
## 214 0 0 1002 1|12 low Small-Large isolated
## 215 0 0 1002 1|12 low Small-Large isolated
## 216 0 0 1002 1|12 low Small-Large isolated
## 217 1 4 1002 1|12 low Small-Large isolated
## 218 1 4 1002 1|12 low Small-Large isolated
## 219 1 4 1002 1|12 low Small-Large isolated
## 220 1 4 1002 1|12 low Small-Large isolated
## 221 1 4 1002 1|12 low Small-Large isolated
## 222 1 4 1002 1|12 low Small-Large isolated
## 223 1 4 1002 1|12 low Small-Large isolated
## 224 1 4 1002 1|12 low Small-Large isolated
## 225 1 4 1002 1|12 low Small-Large isolated
## 226 1 4 1002 1|12 low Small-Large isolated
## 227 1 4 1002 1|12 low Small-Large isolated
## 228 1 4 1002 1|12 low Small-Large isolated
## 229 1 4 1002 1|12 low Small-Large isolated
## 230 1 4 1002 1|12 low Small-Large isolated
## 231 1 4 1002 1|12 low Small-Large isolated
## 232 1 4 1002 1|12 low Small-Large isolated
## 233 1 4 1002 1|12 low Small-Large isolated
## 234 1 4 1002 1|12 low Small-Large isolated
## 235 1 4 1002 1|12 low Small-Large isolated
## 236 1 4 1002 1|12 low Small-Large isolated
## 237 1 4 1002 1|12 low Small-Large isolated
## 238 1 4 1002 1|12 low Small-Large isolated
## 239 1 4 1002 1|12 low Small-Large isolated
## 240 1 4 1002 1|12 low Small-Large isolated
## 241 2 8 1002 1|12 low Small-Large isolated
## 242 2 8 1002 1|12 low Small-Large isolated
## 243 2 8 1002 1|12 low Small-Large isolated
## 244 2 8 1002 1|12 low Small-Large isolated
## 245 2 8 1002 1|12 low Small-Large isolated
## 246 2 8 1002 1|12 low Small-Large isolated
## 247 2 8 1002 1|12 low Small-Large isolated
## 248 2 8 1002 1|12 low Small-Large isolated
## 249 2 8 1002 1|12 low Small-Large isolated
## 250 2 8 1002 1|12 low Small-Large isolated
## 251 2 8 1002 1|12 low Small-Large isolated
## 252 2 8 1002 1|12 low Small-Large isolated
## 253 2 8 1002 1|12 low Small-Large isolated
## 254 2 8 1002 1|12 low Small-Large isolated
## 255 2 8 1002 1|12 low Small-Large isolated
## 256 2 8 1002 1|12 low Small-Large isolated
## 257 2 8 1002 1|12 low Small-Large isolated
## 258 2 8 1002 1|12 low Small-Large isolated
## 259 2 8 1002 1|12 low Small-Large isolated
## 260 2 8 1002 1|12 low Small-Large isolated
## 261 2 8 1002 1|12 low Small-Large isolated
## 262 2 8 1002 1|12 low Small-Large isolated
## 263 2 8 1002 1|12 low Small-Large isolated
## 264 2 8 1002 1|12 low Small-Large isolated
## 265 3 12 1002 1|12 low Small-Large isolated
## 266 3 12 1002 1|12 low Small-Large isolated
## 267 3 12 1002 1|12 low Small-Large isolated
## 268 3 12 1002 1|12 low Small-Large isolated
## 269 3 12 1002 1|12 low Small-Large isolated
## 270 3 12 1002 1|12 low Small-Large isolated
## 271 3 12 1002 1|12 low Small-Large isolated
## 272 3 12 1002 1|12 low Small-Large isolated
## 273 3 12 1002 1|12 low Small-Large isolated
## 274 3 12 1002 1|12 low Small-Large isolated
## 275 3 12 1002 1|12 low Small-Large isolated
## 276 3 12 1002 1|12 low Small-Large isolated
## 277 3 12 1002 1|12 low Small-Large isolated
## 278 3 12 1002 1|12 low Small-Large isolated
## 279 3 12 1002 1|12 low Small-Large isolated
## 280 3 12 1002 1|12 low Small-Large isolated
## 281 3 12 1002 1|12 low Small-Large isolated
## 282 3 12 1002 1|12 low Small-Large isolated
## 283 3 12 1002 1|12 low Small-Large isolated
## 284 3 12 1002 1|12 low Small-Large isolated
## 285 3 12 1002 1|12 low Small-Large isolated
## 286 3 12 1002 1|12 low Small-Large isolated
## 287 3 12 1002 1|12 low Small-Large isolated
## 288 3 12 1002 1|12 low Small-Large isolated
## 289 4 16 1002 1|12 low Small-Large isolated
## 290 4 16 1002 1|12 low Small-Large isolated
## 291 4 16 1002 1|12 low Small-Large isolated
## 292 4 16 1002 1|12 low Small-Large isolated
## 293 4 16 1002 1|12 low Small-Large isolated
## 294 4 16 1002 1|12 low Small-Large isolated
## 295 4 16 1002 1|12 low Small-Large isolated
## 296 4 16 1002 1|12 low Small-Large isolated
## 297 4 16 1002 1|12 low Small-Large isolated
## 298 4 16 1002 1|12 low Small-Large isolated
## 299 4 16 1002 1|12 low Small-Large isolated
## 300 4 16 1002 1|12 low Small-Large isolated
## 301 4 16 1002 1|12 low Small-Large isolated
## 302 4 16 1002 1|12 low Small-Large isolated
## 303 4 16 1002 1|12 low Small-Large isolated
## 304 4 16 1002 1|12 low Small-Large isolated
## 305 4 16 1002 1|12 low Small-Large isolated
## 306 4 16 1002 1|12 low Small-Large isolated
## 307 4 16 1002 1|12 low Small-Large isolated
## 308 4 16 1002 1|12 low Small-Large isolated
## 309 4 16 1002 1|12 low Small-Large isolated
## 310 4 16 1002 1|12 low Small-Large isolated
## 311 4 16 1002 1|12 low Small-Large isolated
## 312 4 16 1002 1|12 low Small-Large isolated
## 313 5 20 1002 1|12 low Small-Large isolated
## 314 5 20 1002 1|12 low Small-Large isolated
## 315 5 20 1002 1|12 low Small-Large isolated
## 316 5 20 1002 1|12 low Small-Large isolated
## 317 5 20 1002 1|12 low Small-Large isolated
## 318 5 20 1002 1|12 low Small-Large isolated
## 319 5 20 1002 1|12 low Small-Large isolated
## 320 5 20 1002 1|12 low Small-Large isolated
## 321 5 20 1002 1|12 low Small-Large isolated
## 322 5 20 1002 1|12 low Small-Large isolated
## 323 5 20 1002 1|12 low Small-Large isolated
## 324 5 20 1002 1|12 low Small-Large isolated
## 325 5 20 1002 1|12 low Small-Large isolated
## 326 5 20 1002 1|12 low Small-Large isolated
## 327 5 20 1002 1|12 low Small-Large isolated
## 328 5 20 1002 1|12 low Small-Large isolated
## 329 5 20 1002 1|12 low Small-Large isolated
## 330 5 20 1002 1|12 low Small-Large isolated
## 331 5 20 1002 1|12 low Small-Large isolated
## 332 5 20 1002 1|12 low Small-Large isolated
## 333 5 20 1002 1|12 low Small-Large isolated
## 334 5 20 1002 1|12 low Small-Large isolated
## 335 5 20 1002 1|12 low Small-Large isolated
## 336 5 20 1002 1|12 low Small-Large isolated
## 337 6 24 1002 1|12 low Small-Large isolated
## 338 6 24 1002 1|12 low Small-Large isolated
## 339 6 24 1002 1|12 low Small-Large isolated
## 340 6 24 1002 1|12 low Small-Large isolated
## 341 6 24 1002 1|12 low Small-Large isolated
## 342 6 24 1002 1|12 low Small-Large isolated
## 343 6 24 1002 1|12 low Small-Large isolated
## 344 6 24 1002 1|12 low Small-Large isolated
## 345 6 24 1002 1|12 low Small-Large isolated
## 346 6 24 1002 1|12 low Small-Large isolated
## 347 6 24 1002 1|12 low Small-Large isolated
## 348 6 24 1002 1|12 low Small-Large isolated
## 349 6 24 1002 1|12 low Small-Large isolated
## 350 6 24 1002 1|12 low Small-Large isolated
## 351 6 24 1002 1|12 low Small-Large isolated
## 352 6 24 1002 1|12 low Small-Large isolated
## 353 6 24 1002 1|12 low Small-Large isolated
## 354 6 24 1002 1|12 low Small-Large isolated
## 355 6 24 1002 1|12 low Small-Large isolated
## 356 6 24 1002 1|12 low Small-Large isolated
## 357 6 24 1002 1|12 low Small-Large isolated
## 358 6 24 1002 1|12 low Small-Large isolated
## 359 6 24 1002 1|12 low Small-Large isolated
## 360 6 24 1002 1|12 low Small-Large isolated
## 361 7 28 1002 1|12 low Small-Large isolated
## 362 7 28 1002 1|12 low Small-Large isolated
## 363 7 28 1002 1|12 low Small-Large isolated
## 364 7 28 1002 1|12 low Small-Large isolated
## 365 7 28 1002 1|12 low Small-Large isolated
## 366 7 28 1002 1|12 low Small-Large isolated
## 367 7 28 1002 1|12 low Small-Large isolated
## 368 7 28 1002 1|12 low Small-Large isolated
## 369 7 28 1002 1|12 low Small-Large isolated
## 370 7 28 1002 1|12 low Small-Large isolated
## 371 7 28 1002 1|12 low Small-Large isolated
## 372 7 28 1002 1|12 low Small-Large isolated
## 373 7 28 1002 1|12 low Small-Large isolated
## 374 7 28 1002 1|12 low Small-Large isolated
## 375 7 28 1002 1|12 low Small-Large isolated
## 376 7 28 1002 1|12 low Small-Large isolated
## 377 7 28 1002 1|12 low Small-Large isolated
## 378 7 28 1002 1|12 low Small-Large isolated
## 379 7 28 1002 1|12 low Small-Large isolated
## 380 7 28 1002 1|12 low Small-Large isolated
## 381 7 28 1002 1|12 low Small-Large isolated
## 382 7 28 1002 1|12 low Small-Large isolated
## 383 7 28 1002 1|12 low Small-Large isolated
## 384 7 28 1002 1|12 low Small-Large isolated
## 385 0 0 1003 1|13 low Small-Large isolated
## 386 0 0 1003 1|13 low Small-Large isolated
## 387 0 0 1003 1|13 low Small-Large isolated
## 388 0 0 1003 1|13 low Small-Large isolated
## 389 0 0 1003 1|13 low Small-Large isolated
## 390 0 0 1003 1|13 low Small-Large isolated
## 391 0 0 1003 1|13 low Small-Large isolated
## 392 0 0 1003 1|13 low Small-Large isolated
## 393 0 0 1003 1|13 low Small-Large isolated
## 394 0 0 1003 1|13 low Small-Large isolated
## 395 0 0 1003 1|13 low Small-Large isolated
## 396 0 0 1003 1|13 low Small-Large isolated
## 397 0 0 1003 1|13 low Small-Large isolated
## 398 0 0 1003 1|13 low Small-Large isolated
## 399 0 0 1003 1|13 low Small-Large isolated
## 400 0 0 1003 1|13 low Small-Large isolated
## 401 0 0 1003 1|13 low Small-Large isolated
## 402 0 0 1003 1|13 low Small-Large isolated
## 403 0 0 1003 1|13 low Small-Large isolated
## 404 0 0 1003 1|13 low Small-Large isolated
## 405 0 0 1003 1|13 low Small-Large isolated
## 406 0 0 1003 1|13 low Small-Large isolated
## 407 0 0 1003 1|13 low Small-Large isolated
## 408 0 0 1003 1|13 low Small-Large isolated
## 409 1 4 1003 1|13 low Small-Large isolated
## 410 1 4 1003 1|13 low Small-Large isolated
## 411 1 4 1003 1|13 low Small-Large isolated
## 412 1 4 1003 1|13 low Small-Large isolated
## 413 1 4 1003 1|13 low Small-Large isolated
## 414 1 4 1003 1|13 low Small-Large isolated
## 415 1 4 1003 1|13 low Small-Large isolated
## 416 1 4 1003 1|13 low Small-Large isolated
## 417 1 4 1003 1|13 low Small-Large isolated
## 418 1 4 1003 1|13 low Small-Large isolated
## 419 1 4 1003 1|13 low Small-Large isolated
## 420 1 4 1003 1|13 low Small-Large isolated
## 421 1 4 1003 1|13 low Small-Large isolated
## 422 1 4 1003 1|13 low Small-Large isolated
## 423 1 4 1003 1|13 low Small-Large isolated
## 424 1 4 1003 1|13 low Small-Large isolated
## 425 1 4 1003 1|13 low Small-Large isolated
## 426 1 4 1003 1|13 low Small-Large isolated
## 427 1 4 1003 1|13 low Small-Large isolated
## 428 1 4 1003 1|13 low Small-Large isolated
## 429 1 4 1003 1|13 low Small-Large isolated
## 430 1 4 1003 1|13 low Small-Large isolated
## 431 1 4 1003 1|13 low Small-Large isolated
## 432 1 4 1003 1|13 low Small-Large isolated
## 433 2 8 1003 1|13 low Small-Large isolated
## 434 2 8 1003 1|13 low Small-Large isolated
## 435 2 8 1003 1|13 low Small-Large isolated
## 436 2 8 1003 1|13 low Small-Large isolated
## 437 2 8 1003 1|13 low Small-Large isolated
## 438 2 8 1003 1|13 low Small-Large isolated
## 439 2 8 1003 1|13 low Small-Large isolated
## 440 2 8 1003 1|13 low Small-Large isolated
## 441 2 8 1003 1|13 low Small-Large isolated
## 442 2 8 1003 1|13 low Small-Large isolated
## 443 2 8 1003 1|13 low Small-Large isolated
## 444 2 8 1003 1|13 low Small-Large isolated
## 445 2 8 1003 1|13 low Small-Large isolated
## 446 2 8 1003 1|13 low Small-Large isolated
## 447 2 8 1003 1|13 low Small-Large isolated
## 448 2 8 1003 1|13 low Small-Large isolated
## 449 2 8 1003 1|13 low Small-Large isolated
## 450 2 8 1003 1|13 low Small-Large isolated
## 451 2 8 1003 1|13 low Small-Large isolated
## 452 2 8 1003 1|13 low Small-Large isolated
## 453 2 8 1003 1|13 low Small-Large isolated
## 454 2 8 1003 1|13 low Small-Large isolated
## 455 2 8 1003 1|13 low Small-Large isolated
## 456 2 8 1003 1|13 low Small-Large isolated
## 457 3 12 1003 1|13 low Small-Large isolated
## 458 3 12 1003 1|13 low Small-Large isolated
## 459 3 12 1003 1|13 low Small-Large isolated
## 460 3 12 1003 1|13 low Small-Large isolated
## 461 3 12 1003 1|13 low Small-Large isolated
## 462 3 12 1003 1|13 low Small-Large isolated
## 463 3 12 1003 1|13 low Small-Large isolated
## 464 3 12 1003 1|13 low Small-Large isolated
## 465 3 12 1003 1|13 low Small-Large isolated
## 466 3 12 1003 1|13 low Small-Large isolated
## 467 3 12 1003 1|13 low Small-Large isolated
## 468 3 12 1003 1|13 low Small-Large isolated
## 469 3 12 1003 1|13 low Small-Large isolated
## 470 3 12 1003 1|13 low Small-Large isolated
## 471 3 12 1003 1|13 low Small-Large isolated
## 472 3 12 1003 1|13 low Small-Large isolated
## 473 3 12 1003 1|13 low Small-Large isolated
## 474 3 12 1003 1|13 low Small-Large isolated
## 475 3 12 1003 1|13 low Small-Large isolated
## 476 3 12 1003 1|13 low Small-Large isolated
## 477 3 12 1003 1|13 low Small-Large isolated
## 478 3 12 1003 1|13 low Small-Large isolated
## 479 3 12 1003 1|13 low Small-Large isolated
## 480 3 12 1003 1|13 low Small-Large isolated
## 481 4 16 1003 1|13 low Small-Large isolated
## 482 4 16 1003 1|13 low Small-Large isolated
## 483 4 16 1003 1|13 low Small-Large isolated
## 484 4 16 1003 1|13 low Small-Large isolated
## 485 4 16 1003 1|13 low Small-Large isolated
## 486 4 16 1003 1|13 low Small-Large isolated
## 487 4 16 1003 1|13 low Small-Large isolated
## 488 4 16 1003 1|13 low Small-Large isolated
## 489 4 16 1003 1|13 low Small-Large isolated
## 490 4 16 1003 1|13 low Small-Large isolated
## 491 4 16 1003 1|13 low Small-Large isolated
## 492 4 16 1003 1|13 low Small-Large isolated
## 493 4 16 1003 1|13 low Small-Large isolated
## 494 4 16 1003 1|13 low Small-Large isolated
## 495 4 16 1003 1|13 low Small-Large isolated
## 496 4 16 1003 1|13 low Small-Large isolated
## 497 4 16 1003 1|13 low Small-Large isolated
## 498 4 16 1003 1|13 low Small-Large isolated
## 499 4 16 1003 1|13 low Small-Large isolated
## 500 4 16 1003 1|13 low Small-Large isolated
## 501 4 16 1003 1|13 low Small-Large isolated
## 502 4 16 1003 1|13 low Small-Large isolated
## 503 4 16 1003 1|13 low Small-Large isolated
## 504 4 16 1003 1|13 low Small-Large isolated
## 505 5 20 1003 1|13 low Small-Large isolated
## 506 5 20 1003 1|13 low Small-Large isolated
## 507 5 20 1003 1|13 low Small-Large isolated
## 508 5 20 1003 1|13 low Small-Large isolated
## 509 5 20 1003 1|13 low Small-Large isolated
## 510 5 20 1003 1|13 low Small-Large isolated
## 511 5 20 1003 1|13 low Small-Large isolated
## 512 5 20 1003 1|13 low Small-Large isolated
## 513 5 20 1003 1|13 low Small-Large isolated
## 514 5 20 1003 1|13 low Small-Large isolated
## 515 5 20 1003 1|13 low Small-Large isolated
## 516 5 20 1003 1|13 low Small-Large isolated
## 517 5 20 1003 1|13 low Small-Large isolated
## 518 5 20 1003 1|13 low Small-Large isolated
## 519 5 20 1003 1|13 low Small-Large isolated
## 520 5 20 1003 1|13 low Small-Large isolated
## 521 5 20 1003 1|13 low Small-Large isolated
## 522 5 20 1003 1|13 low Small-Large isolated
## 523 5 20 1003 1|13 low Small-Large isolated
## 524 5 20 1003 1|13 low Small-Large isolated
## 525 5 20 1003 1|13 low Small-Large isolated
## 526 5 20 1003 1|13 low Small-Large isolated
## 527 5 20 1003 1|13 low Small-Large isolated
## 528 5 20 1003 1|13 low Small-Large isolated
## 529 6 24 1003 1|13 low Small-Large isolated
## 530 6 24 1003 1|13 low Small-Large isolated
## 531 6 24 1003 1|13 low Small-Large isolated
## 532 6 24 1003 1|13 low Small-Large isolated
## 533 6 24 1003 1|13 low Small-Large isolated
## 534 6 24 1003 1|13 low Small-Large isolated
## 535 6 24 1003 1|13 low Small-Large isolated
## 536 6 24 1003 1|13 low Small-Large isolated
## 537 6 24 1003 1|13 low Small-Large isolated
## 538 6 24 1003 1|13 low Small-Large isolated
## 539 6 24 1003 1|13 low Small-Large isolated
## 540 6 24 1003 1|13 low Small-Large isolated
## 541 6 24 1003 1|13 low Small-Large isolated
## 542 6 24 1003 1|13 low Small-Large isolated
## 543 6 24 1003 1|13 low Small-Large isolated
## 544 6 24 1003 1|13 low Small-Large isolated
## 545 6 24 1003 1|13 low Small-Large isolated
## 546 6 24 1003 1|13 low Small-Large isolated
## 547 6 24 1003 1|13 low Small-Large isolated
## 548 6 24 1003 1|13 low Small-Large isolated
## 549 6 24 1003 1|13 low Small-Large isolated
## 550 6 24 1003 1|13 low Small-Large isolated
## 551 6 24 1003 1|13 low Small-Large isolated
## 552 6 24 1003 1|13 low Small-Large isolated
## 553 7 28 1003 1|13 low Small-Large isolated
## 554 7 28 1003 1|13 low Small-Large isolated
## 555 7 28 1003 1|13 low Small-Large isolated
## 556 7 28 1003 1|13 low Small-Large isolated
## 557 7 28 1003 1|13 low Small-Large isolated
## 558 7 28 1003 1|13 low Small-Large isolated
## 559 7 28 1003 1|13 low Small-Large isolated
## 560 7 28 1003 1|13 low Small-Large isolated
## 561 7 28 1003 1|13 low Small-Large isolated
## 562 7 28 1003 1|13 low Small-Large isolated
## 563 7 28 1003 1|13 low Small-Large isolated
## 564 7 28 1003 1|13 low Small-Large isolated
## 565 7 28 1003 1|13 low Small-Large isolated
## 566 7 28 1003 1|13 low Small-Large isolated
## 567 7 28 1003 1|13 low Small-Large isolated
## 568 7 28 1003 1|13 low Small-Large isolated
## 569 7 28 1003 1|13 low Small-Large isolated
## 570 7 28 1003 1|13 low Small-Large isolated
## 571 7 28 1003 1|13 low Small-Large isolated
## 572 7 28 1003 1|13 low Small-Large isolated
## 573 7 28 1003 1|13 low Small-Large isolated
## 574 7 28 1003 1|13 low Small-Large isolated
## 575 7 28 1003 1|13 low Small-Large isolated
## 576 7 28 1003 1|13 low Small-Large isolated
## 577 0 0 1004 1|14 low Small-Large isolated
## 578 0 0 1004 1|14 low Small-Large isolated
## 579 0 0 1004 1|14 low Small-Large isolated
## 580 0 0 1004 1|14 low Small-Large isolated
## 581 0 0 1004 1|14 low Small-Large isolated
## 582 0 0 1004 1|14 low Small-Large isolated
## 583 0 0 1004 1|14 low Small-Large isolated
## 584 0 0 1004 1|14 low Small-Large isolated
## 585 0 0 1004 1|14 low Small-Large isolated
## 586 0 0 1004 1|14 low Small-Large isolated
## 587 0 0 1004 1|14 low Small-Large isolated
## 588 0 0 1004 1|14 low Small-Large isolated
## 589 0 0 1004 1|14 low Small-Large isolated
## 590 0 0 1004 1|14 low Small-Large isolated
## 591 0 0 1004 1|14 low Small-Large isolated
## 592 0 0 1004 1|14 low Small-Large isolated
## 593 0 0 1004 1|14 low Small-Large isolated
## 594 0 0 1004 1|14 low Small-Large isolated
## 595 0 0 1004 1|14 low Small-Large isolated
## 596 0 0 1004 1|14 low Small-Large isolated
## 597 0 0 1004 1|14 low Small-Large isolated
## 598 0 0 1004 1|14 low Small-Large isolated
## 599 0 0 1004 1|14 low Small-Large isolated
## 600 0 0 1004 1|14 low Small-Large isolated
## 601 1 4 1004 1|14 low Small-Large isolated
## 602 1 4 1004 1|14 low Small-Large isolated
## 603 1 4 1004 1|14 low Small-Large isolated
## 604 1 4 1004 1|14 low Small-Large isolated
## 605 1 4 1004 1|14 low Small-Large isolated
## 606 1 4 1004 1|14 low Small-Large isolated
## 607 1 4 1004 1|14 low Small-Large isolated
## 608 1 4 1004 1|14 low Small-Large isolated
## 609 1 4 1004 1|14 low Small-Large isolated
## 610 1 4 1004 1|14 low Small-Large isolated
## 611 1 4 1004 1|14 low Small-Large isolated
## 612 1 4 1004 1|14 low Small-Large isolated
## 613 1 4 1004 1|14 low Small-Large isolated
## 614 1 4 1004 1|14 low Small-Large isolated
## 615 1 4 1004 1|14 low Small-Large isolated
## 616 1 4 1004 1|14 low Small-Large isolated
## 617 1 4 1004 1|14 low Small-Large isolated
## 618 1 4 1004 1|14 low Small-Large isolated
## 619 1 4 1004 1|14 low Small-Large isolated
## 620 1 4 1004 1|14 low Small-Large isolated
## 621 1 4 1004 1|14 low Small-Large isolated
## 622 1 4 1004 1|14 low Small-Large isolated
## 623 1 4 1004 1|14 low Small-Large isolated
## 624 1 4 1004 1|14 low Small-Large isolated
## 625 2 8 1004 1|14 low Small-Large isolated
## 626 2 8 1004 1|14 low Small-Large isolated
## 627 2 8 1004 1|14 low Small-Large isolated
## 628 2 8 1004 1|14 low Small-Large isolated
## 629 2 8 1004 1|14 low Small-Large isolated
## 630 2 8 1004 1|14 low Small-Large isolated
## 631 2 8 1004 1|14 low Small-Large isolated
## 632 2 8 1004 1|14 low Small-Large isolated
## 633 2 8 1004 1|14 low Small-Large isolated
## 634 2 8 1004 1|14 low Small-Large isolated
## 635 2 8 1004 1|14 low Small-Large isolated
## 636 2 8 1004 1|14 low Small-Large isolated
## 637 2 8 1004 1|14 low Small-Large isolated
## 638 2 8 1004 1|14 low Small-Large isolated
## 639 2 8 1004 1|14 low Small-Large isolated
## 640 2 8 1004 1|14 low Small-Large isolated
## 641 2 8 1004 1|14 low Small-Large isolated
## 642 2 8 1004 1|14 low Small-Large isolated
## 643 2 8 1004 1|14 low Small-Large isolated
## 644 2 8 1004 1|14 low Small-Large isolated
## 645 2 8 1004 1|14 low Small-Large isolated
## 646 2 8 1004 1|14 low Small-Large isolated
## 647 2 8 1004 1|14 low Small-Large isolated
## 648 2 8 1004 1|14 low Small-Large isolated
## 649 3 12 1004 1|14 low Small-Large isolated
## 650 3 12 1004 1|14 low Small-Large isolated
## 651 3 12 1004 1|14 low Small-Large isolated
## 652 3 12 1004 1|14 low Small-Large isolated
## 653 3 12 1004 1|14 low Small-Large isolated
## 654 3 12 1004 1|14 low Small-Large isolated
## 655 3 12 1004 1|14 low Small-Large isolated
## 656 3 12 1004 1|14 low Small-Large isolated
## 657 3 12 1004 1|14 low Small-Large isolated
## 658 3 12 1004 1|14 low Small-Large isolated
## 659 3 12 1004 1|14 low Small-Large isolated
## 660 3 12 1004 1|14 low Small-Large isolated
## 661 3 12 1004 1|14 low Small-Large isolated
## 662 3 12 1004 1|14 low Small-Large isolated
## 663 3 12 1004 1|14 low Small-Large isolated
## 664 3 12 1004 1|14 low Small-Large isolated
## 665 3 12 1004 1|14 low Small-Large isolated
## 666 3 12 1004 1|14 low Small-Large isolated
## 667 3 12 1004 1|14 low Small-Large isolated
## 668 3 12 1004 1|14 low Small-Large isolated
## 669 3 12 1004 1|14 low Small-Large isolated
## 670 3 12 1004 1|14 low Small-Large isolated
## 671 3 12 1004 1|14 low Small-Large isolated
## 672 3 12 1004 1|14 low Small-Large isolated
## 673 4 16 1004 1|14 low Small-Large isolated
## 674 4 16 1004 1|14 low Small-Large isolated
## 675 4 16 1004 1|14 low Small-Large isolated
## 676 4 16 1004 1|14 low Small-Large isolated
## 677 4 16 1004 1|14 low Small-Large isolated
## 678 4 16 1004 1|14 low Small-Large isolated
## 679 4 16 1004 1|14 low Small-Large isolated
## 680 4 16 1004 1|14 low Small-Large isolated
## 681 4 16 1004 1|14 low Small-Large isolated
## 682 4 16 1004 1|14 low Small-Large isolated
## 683 4 16 1004 1|14 low Small-Large isolated
## 684 4 16 1004 1|14 low Small-Large isolated
## 685 4 16 1004 1|14 low Small-Large isolated
## 686 4 16 1004 1|14 low Small-Large isolated
## 687 4 16 1004 1|14 low Small-Large isolated
## 688 4 16 1004 1|14 low Small-Large isolated
## 689 4 16 1004 1|14 low Small-Large isolated
## 690 4 16 1004 1|14 low Small-Large isolated
## 691 4 16 1004 1|14 low Small-Large isolated
## 692 4 16 1004 1|14 low Small-Large isolated
## 693 4 16 1004 1|14 low Small-Large isolated
## 694 4 16 1004 1|14 low Small-Large isolated
## 695 4 16 1004 1|14 low Small-Large isolated
## 696 4 16 1004 1|14 low Small-Large isolated
## 697 5 20 1004 1|14 low Small-Large isolated
## 698 5 20 1004 1|14 low Small-Large isolated
## 699 5 20 1004 1|14 low Small-Large isolated
## 700 5 20 1004 1|14 low Small-Large isolated
## 701 5 20 1004 1|14 low Small-Large isolated
## 702 5 20 1004 1|14 low Small-Large isolated
## 703 5 20 1004 1|14 low Small-Large isolated
## 704 5 20 1004 1|14 low Small-Large isolated
## 705 5 20 1004 1|14 low Small-Large isolated
## 706 5 20 1004 1|14 low Small-Large isolated
## 707 5 20 1004 1|14 low Small-Large isolated
## 708 5 20 1004 1|14 low Small-Large isolated
## 709 5 20 1004 1|14 low Small-Large isolated
## 710 5 20 1004 1|14 low Small-Large isolated
## 711 5 20 1004 1|14 low Small-Large isolated
## 712 5 20 1004 1|14 low Small-Large isolated
## 713 5 20 1004 1|14 low Small-Large isolated
## 714 5 20 1004 1|14 low Small-Large isolated
## 715 5 20 1004 1|14 low Small-Large isolated
## 716 5 20 1004 1|14 low Small-Large isolated
## 717 5 20 1004 1|14 low Small-Large isolated
## 718 5 20 1004 1|14 low Small-Large isolated
## 719 5 20 1004 1|14 low Small-Large isolated
## 720 5 20 1004 1|14 low Small-Large isolated
## 721 6 24 1004 1|14 low Small-Large isolated
## 722 6 24 1004 1|14 low Small-Large isolated
## 723 6 24 1004 1|14 low Small-Large isolated
## 724 6 24 1004 1|14 low Small-Large isolated
## 725 6 24 1004 1|14 low Small-Large isolated
## 726 6 24 1004 1|14 low Small-Large isolated
## 727 6 24 1004 1|14 low Small-Large isolated
## 728 6 24 1004 1|14 low Small-Large isolated
## 729 6 24 1004 1|14 low Small-Large isolated
## 730 6 24 1004 1|14 low Small-Large isolated
## 731 6 24 1004 1|14 low Small-Large isolated
## 732 6 24 1004 1|14 low Small-Large isolated
## 733 6 24 1004 1|14 low Small-Large isolated
## 734 6 24 1004 1|14 low Small-Large isolated
## 735 6 24 1004 1|14 low Small-Large isolated
## 736 6 24 1004 1|14 low Small-Large isolated
## 737 6 24 1004 1|14 low Small-Large isolated
## 738 6 24 1004 1|14 low Small-Large isolated
## 739 6 24 1004 1|14 low Small-Large isolated
## 740 6 24 1004 1|14 low Small-Large isolated
## 741 6 24 1004 1|14 low Small-Large isolated
## 742 6 24 1004 1|14 low Small-Large isolated
## 743 6 24 1004 1|14 low Small-Large isolated
## 744 6 24 1004 1|14 low Small-Large isolated
## 745 7 28 1004 1|14 low Small-Large isolated
## 746 7 28 1004 1|14 low Small-Large isolated
## 747 7 28 1004 1|14 low Small-Large isolated
## 748 7 28 1004 1|14 low Small-Large isolated
## 749 7 28 1004 1|14 low Small-Large isolated
## 750 7 28 1004 1|14 low Small-Large isolated
## 751 7 28 1004 1|14 low Small-Large isolated
## 752 7 28 1004 1|14 low Small-Large isolated
## 753 7 28 1004 1|14 low Small-Large isolated
## 754 7 28 1004 1|14 low Small-Large isolated
## 755 7 28 1004 1|14 low Small-Large isolated
## 756 7 28 1004 1|14 low Small-Large isolated
## 757 7 28 1004 1|14 low Small-Large isolated
## 758 7 28 1004 1|14 low Small-Large isolated
## 759 7 28 1004 1|14 low Small-Large isolated
## 760 7 28 1004 1|14 low Small-Large isolated
## 761 7 28 1004 1|14 low Small-Large isolated
## 762 7 28 1004 1|14 low Small-Large isolated
## 763 7 28 1004 1|14 low Small-Large isolated
## 764 7 28 1004 1|14 low Small-Large isolated
## 765 7 28 1004 1|14 low Small-Large isolated
## 766 7 28 1004 1|14 low Small-Large isolated
## 767 7 28 1004 1|14 low Small-Large isolated
## 768 7 28 1004 1|14 low Small-Large isolated
## 769 0 0 1005 1|15 low Small-Large isolated
## 770 0 0 1005 1|15 low Small-Large isolated
## 771 0 0 1005 1|15 low Small-Large isolated
## 772 0 0 1005 1|15 low Small-Large isolated
## 773 0 0 1005 1|15 low Small-Large isolated
## 774 0 0 1005 1|15 low Small-Large isolated
## 775 0 0 1005 1|15 low Small-Large isolated
## 776 0 0 1005 1|15 low Small-Large isolated
## 777 0 0 1005 1|15 low Small-Large isolated
## 778 0 0 1005 1|15 low Small-Large isolated
## 779 0 0 1005 1|15 low Small-Large isolated
## 780 0 0 1005 1|15 low Small-Large isolated
## 781 0 0 1005 1|15 low Small-Large isolated
## 782 0 0 1005 1|15 low Small-Large isolated
## 783 0 0 1005 1|15 low Small-Large isolated
## 784 0 0 1005 1|15 low Small-Large isolated
## 785 0 0 1005 1|15 low Small-Large isolated
## 786 0 0 1005 1|15 low Small-Large isolated
## 787 0 0 1005 1|15 low Small-Large isolated
## 788 0 0 1005 1|15 low Small-Large isolated
## 789 0 0 1005 1|15 low Small-Large isolated
## 790 0 0 1005 1|15 low Small-Large isolated
## 791 0 0 1005 1|15 low Small-Large isolated
## 792 0 0 1005 1|15 low Small-Large isolated
## 793 1 4 1005 1|15 low Small-Large isolated
## 794 1 4 1005 1|15 low Small-Large isolated
## 795 1 4 1005 1|15 low Small-Large isolated
## 796 1 4 1005 1|15 low Small-Large isolated
## 797 1 4 1005 1|15 low Small-Large isolated
## 798 1 4 1005 1|15 low Small-Large isolated
## 799 1 4 1005 1|15 low Small-Large isolated
## 800 1 4 1005 1|15 low Small-Large isolated
## 801 1 4 1005 1|15 low Small-Large isolated
## 802 1 4 1005 1|15 low Small-Large isolated
## 803 1 4 1005 1|15 low Small-Large isolated
## 804 1 4 1005 1|15 low Small-Large isolated
## 805 1 4 1005 1|15 low Small-Large isolated
## 806 1 4 1005 1|15 low Small-Large isolated
## 807 1 4 1005 1|15 low Small-Large isolated
## 808 1 4 1005 1|15 low Small-Large isolated
## 809 1 4 1005 1|15 low Small-Large isolated
## 810 1 4 1005 1|15 low Small-Large isolated
## 811 1 4 1005 1|15 low Small-Large isolated
## 812 1 4 1005 1|15 low Small-Large isolated
## 813 1 4 1005 1|15 low Small-Large isolated
## 814 1 4 1005 1|15 low Small-Large isolated
## 815 1 4 1005 1|15 low Small-Large isolated
## 816 1 4 1005 1|15 low Small-Large isolated
## 817 2 8 1005 1|15 low Small-Large isolated
## 818 2 8 1005 1|15 low Small-Large isolated
## 819 2 8 1005 1|15 low Small-Large isolated
## 820 2 8 1005 1|15 low Small-Large isolated
## 821 2 8 1005 1|15 low Small-Large isolated
## 822 2 8 1005 1|15 low Small-Large isolated
## 823 2 8 1005 1|15 low Small-Large isolated
## 824 2 8 1005 1|15 low Small-Large isolated
## 825 2 8 1005 1|15 low Small-Large isolated
## 826 2 8 1005 1|15 low Small-Large isolated
## 827 2 8 1005 1|15 low Small-Large isolated
## 828 2 8 1005 1|15 low Small-Large isolated
## 829 2 8 1005 1|15 low Small-Large isolated
## 830 2 8 1005 1|15 low Small-Large isolated
## 831 2 8 1005 1|15 low Small-Large isolated
## 832 2 8 1005 1|15 low Small-Large isolated
## 833 2 8 1005 1|15 low Small-Large isolated
## 834 2 8 1005 1|15 low Small-Large isolated
## 835 2 8 1005 1|15 low Small-Large isolated
## 836 2 8 1005 1|15 low Small-Large isolated
## 837 2 8 1005 1|15 low Small-Large isolated
## 838 2 8 1005 1|15 low Small-Large isolated
## 839 2 8 1005 1|15 low Small-Large isolated
## 840 2 8 1005 1|15 low Small-Large isolated
## 841 3 12 1005 1|15 low Small-Large isolated
## 842 3 12 1005 1|15 low Small-Large isolated
## 843 3 12 1005 1|15 low Small-Large isolated
## 844 3 12 1005 1|15 low Small-Large isolated
## 845 3 12 1005 1|15 low Small-Large isolated
## 846 3 12 1005 1|15 low Small-Large isolated
## 847 3 12 1005 1|15 low Small-Large isolated
## 848 3 12 1005 1|15 low Small-Large isolated
## 849 3 12 1005 1|15 low Small-Large isolated
## 850 3 12 1005 1|15 low Small-Large isolated
## 851 3 12 1005 1|15 low Small-Large isolated
## 852 3 12 1005 1|15 low Small-Large isolated
## 853 3 12 1005 1|15 low Small-Large isolated
## 854 3 12 1005 1|15 low Small-Large isolated
## 855 3 12 1005 1|15 low Small-Large isolated
## 856 3 12 1005 1|15 low Small-Large isolated
## 857 3 12 1005 1|15 low Small-Large isolated
## 858 3 12 1005 1|15 low Small-Large isolated
## 859 3 12 1005 1|15 low Small-Large isolated
## 860 3 12 1005 1|15 low Small-Large isolated
## 861 3 12 1005 1|15 low Small-Large isolated
## 862 3 12 1005 1|15 low Small-Large isolated
## 863 3 12 1005 1|15 low Small-Large isolated
## 864 3 12 1005 1|15 low Small-Large isolated
## 865 4 16 1005 1|15 low Small-Large isolated
## 866 4 16 1005 1|15 low Small-Large isolated
## 867 4 16 1005 1|15 low Small-Large isolated
## 868 4 16 1005 1|15 low Small-Large isolated
## 869 4 16 1005 1|15 low Small-Large isolated
## 870 4 16 1005 1|15 low Small-Large isolated
## 871 4 16 1005 1|15 low Small-Large isolated
## 872 4 16 1005 1|15 low Small-Large isolated
## 873 4 16 1005 1|15 low Small-Large isolated
## 874 4 16 1005 1|15 low Small-Large isolated
## 875 4 16 1005 1|15 low Small-Large isolated
## 876 4 16 1005 1|15 low Small-Large isolated
## 877 4 16 1005 1|15 low Small-Large isolated
## 878 4 16 1005 1|15 low Small-Large isolated
## 879 4 16 1005 1|15 low Small-Large isolated
## 880 4 16 1005 1|15 low Small-Large isolated
## 881 4 16 1005 1|15 low Small-Large isolated
## 882 4 16 1005 1|15 low Small-Large isolated
## 883 4 16 1005 1|15 low Small-Large isolated
## 884 4 16 1005 1|15 low Small-Large isolated
## 885 4 16 1005 1|15 low Small-Large isolated
## 886 4 16 1005 1|15 low Small-Large isolated
## 887 4 16 1005 1|15 low Small-Large isolated
## 888 4 16 1005 1|15 low Small-Large isolated
## 889 5 20 1005 1|15 low Small-Large isolated
## 890 5 20 1005 1|15 low Small-Large isolated
## 891 5 20 1005 1|15 low Small-Large isolated
## 892 5 20 1005 1|15 low Small-Large isolated
## 893 5 20 1005 1|15 low Small-Large isolated
## 894 5 20 1005 1|15 low Small-Large isolated
## 895 5 20 1005 1|15 low Small-Large isolated
## 896 5 20 1005 1|15 low Small-Large isolated
## 897 5 20 1005 1|15 low Small-Large isolated
## 898 5 20 1005 1|15 low Small-Large isolated
## 899 5 20 1005 1|15 low Small-Large isolated
## 900 5 20 1005 1|15 low Small-Large isolated
## 901 5 20 1005 1|15 low Small-Large isolated
## 902 5 20 1005 1|15 low Small-Large isolated
## 903 5 20 1005 1|15 low Small-Large isolated
## 904 5 20 1005 1|15 low Small-Large isolated
## 905 5 20 1005 1|15 low Small-Large isolated
## 906 5 20 1005 1|15 low Small-Large isolated
## 907 5 20 1005 1|15 low Small-Large isolated
## 908 5 20 1005 1|15 low Small-Large isolated
## 909 5 20 1005 1|15 low Small-Large isolated
## 910 5 20 1005 1|15 low Small-Large isolated
## 911 5 20 1005 1|15 low Small-Large isolated
## 912 5 20 1005 1|15 low Small-Large isolated
## 913 6 24 1005 1|15 low Small-Large isolated
## 914 6 24 1005 1|15 low Small-Large isolated
## 915 6 24 1005 1|15 low Small-Large isolated
## 916 6 24 1005 1|15 low Small-Large isolated
## 917 6 24 1005 1|15 low Small-Large isolated
## 918 6 24 1005 1|15 low Small-Large isolated
## 919 6 24 1005 1|15 low Small-Large isolated
## 920 6 24 1005 1|15 low Small-Large isolated
## 921 6 24 1005 1|15 low Small-Large isolated
## 922 6 24 1005 1|15 low Small-Large isolated
## 923 6 24 1005 1|15 low Small-Large isolated
## 924 6 24 1005 1|15 low Small-Large isolated
## 925 6 24 1005 1|15 low Small-Large isolated
## 926 6 24 1005 1|15 low Small-Large isolated
## 927 6 24 1005 1|15 low Small-Large isolated
## 928 6 24 1005 1|15 low Small-Large isolated
## 929 6 24 1005 1|15 low Small-Large isolated
## 930 6 24 1005 1|15 low Small-Large isolated
## 931 6 24 1005 1|15 low Small-Large isolated
## 932 6 24 1005 1|15 low Small-Large isolated
## 933 6 24 1005 1|15 low Small-Large isolated
## 934 6 24 1005 1|15 low Small-Large isolated
## 935 6 24 1005 1|15 low Small-Large isolated
## 936 6 24 1005 1|15 low Small-Large isolated
## 937 7 28 1005 1|15 low Small-Large isolated
## 938 7 28 1005 1|15 low Small-Large isolated
## 939 7 28 1005 1|15 low Small-Large isolated
## 940 7 28 1005 1|15 low Small-Large isolated
## 941 7 28 1005 1|15 low Small-Large isolated
## 942 7 28 1005 1|15 low Small-Large isolated
## 943 7 28 1005 1|15 low Small-Large isolated
## 944 7 28 1005 1|15 low Small-Large isolated
## 945 7 28 1005 1|15 low Small-Large isolated
## 946 7 28 1005 1|15 low Small-Large isolated
## 947 7 28 1005 1|15 low Small-Large isolated
## 948 7 28 1005 1|15 low Small-Large isolated
## 949 7 28 1005 1|15 low Small-Large isolated
## 950 7 28 1005 1|15 low Small-Large isolated
## 951 7 28 1005 1|15 low Small-Large isolated
## 952 7 28 1005 1|15 low Small-Large isolated
## 953 7 28 1005 1|15 low Small-Large isolated
## 954 7 28 1005 1|15 low Small-Large isolated
## 955 7 28 1005 1|15 low Small-Large isolated
## 956 7 28 1005 1|15 low Small-Large isolated
## 957 7 28 1005 1|15 low Small-Large isolated
## 958 7 28 1005 1|15 low Small-Large isolated
## 959 7 28 1005 1|15 low Small-Large isolated
## 960 7 28 1005 1|15 low Small-Large isolated
## 961 0 0 1006 2|11 low Small-Large isolated
## 962 0 0 1006 2|11 low Small-Large isolated
## 963 0 0 1006 2|11 low Small-Large isolated
## 964 0 0 1006 2|11 low Small-Large isolated
## 965 0 0 1006 2|11 low Small-Large isolated
## 966 0 0 1006 2|11 low Small-Large isolated
## 967 0 0 1006 2|11 low Small-Large isolated
## 968 0 0 1006 2|11 low Small-Large isolated
## 969 0 0 1006 2|11 low Small-Large isolated
## 970 0 0 1006 2|11 low Small-Large isolated
## 971 0 0 1006 2|11 low Small-Large isolated
## 972 0 0 1006 2|11 low Small-Large isolated
## 973 0 0 1006 2|11 low Small-Large isolated
## 974 0 0 1006 2|11 low Small-Large isolated
## 975 0 0 1006 2|11 low Small-Large isolated
## 976 0 0 1006 2|11 low Small-Large isolated
## 977 0 0 1006 2|11 low Small-Large isolated
## 978 0 0 1006 2|11 low Small-Large isolated
## 979 0 0 1006 2|11 low Small-Large isolated
## 980 0 0 1006 2|11 low Small-Large isolated
## 981 0 0 1006 2|11 low Small-Large isolated
## 982 0 0 1006 2|11 low Small-Large isolated
## 983 0 0 1006 2|11 low Small-Large isolated
## 984 0 0 1006 2|11 low Small-Large isolated
## 985 1 4 1006 2|11 low Small-Large isolated
## 986 1 4 1006 2|11 low Small-Large isolated
## 987 1 4 1006 2|11 low Small-Large isolated
## 988 1 4 1006 2|11 low Small-Large isolated
## 989 1 4 1006 2|11 low Small-Large isolated
## 990 1 4 1006 2|11 low Small-Large isolated
## 991 1 4 1006 2|11 low Small-Large isolated
## 992 1 4 1006 2|11 low Small-Large isolated
## 993 1 4 1006 2|11 low Small-Large isolated
## 994 1 4 1006 2|11 low Small-Large isolated
## 995 1 4 1006 2|11 low Small-Large isolated
## 996 1 4 1006 2|11 low Small-Large isolated
## 997 1 4 1006 2|11 low Small-Large isolated
## 998 1 4 1006 2|11 low Small-Large isolated
## 999 1 4 1006 2|11 low Small-Large isolated
## 1000 1 4 1006 2|11 low Small-Large isolated
## 1001 1 4 1006 2|11 low Small-Large isolated
## 1002 1 4 1006 2|11 low Small-Large isolated
## 1003 1 4 1006 2|11 low Small-Large isolated
## 1004 1 4 1006 2|11 low Small-Large isolated
## 1005 1 4 1006 2|11 low Small-Large isolated
## 1006 1 4 1006 2|11 low Small-Large isolated
## 1007 1 4 1006 2|11 low Small-Large isolated
## 1008 1 4 1006 2|11 low Small-Large isolated
## 1009 2 8 1006 2|11 low Small-Large isolated
## 1010 2 8 1006 2|11 low Small-Large isolated
## 1011 2 8 1006 2|11 low Small-Large isolated
## 1012 2 8 1006 2|11 low Small-Large isolated
## 1013 2 8 1006 2|11 low Small-Large isolated
## 1014 2 8 1006 2|11 low Small-Large isolated
## 1015 2 8 1006 2|11 low Small-Large isolated
## 1016 2 8 1006 2|11 low Small-Large isolated
## 1017 2 8 1006 2|11 low Small-Large isolated
## 1018 2 8 1006 2|11 low Small-Large isolated
## 1019 2 8 1006 2|11 low Small-Large isolated
## 1020 2 8 1006 2|11 low Small-Large isolated
## 1021 2 8 1006 2|11 low Small-Large isolated
## 1022 2 8 1006 2|11 low Small-Large isolated
## 1023 2 8 1006 2|11 low Small-Large isolated
## 1024 2 8 1006 2|11 low Small-Large isolated
## 1025 2 8 1006 2|11 low Small-Large isolated
## 1026 2 8 1006 2|11 low Small-Large isolated
## 1027 2 8 1006 2|11 low Small-Large isolated
## 1028 2 8 1006 2|11 low Small-Large isolated
## 1029 2 8 1006 2|11 low Small-Large isolated
## 1030 2 8 1006 2|11 low Small-Large isolated
## 1031 2 8 1006 2|11 low Small-Large isolated
## 1032 2 8 1006 2|11 low Small-Large isolated
## 1033 3 12 1006 2|11 low Small-Large isolated
## 1034 3 12 1006 2|11 low Small-Large isolated
## 1035 3 12 1006 2|11 low Small-Large isolated
## 1036 3 12 1006 2|11 low Small-Large isolated
## 1037 3 12 1006 2|11 low Small-Large isolated
## 1038 3 12 1006 2|11 low Small-Large isolated
## 1039 3 12 1006 2|11 low Small-Large isolated
## 1040 3 12 1006 2|11 low Small-Large isolated
## 1041 3 12 1006 2|11 low Small-Large isolated
## 1042 3 12 1006 2|11 low Small-Large isolated
## 1043 3 12 1006 2|11 low Small-Large isolated
## 1044 3 12 1006 2|11 low Small-Large isolated
## 1045 3 12 1006 2|11 low Small-Large isolated
## 1046 3 12 1006 2|11 low Small-Large isolated
## 1047 3 12 1006 2|11 low Small-Large isolated
## 1048 3 12 1006 2|11 low Small-Large isolated
## 1049 3 12 1006 2|11 low Small-Large isolated
## 1050 3 12 1006 2|11 low Small-Large isolated
## 1051 3 12 1006 2|11 low Small-Large isolated
## 1052 3 12 1006 2|11 low Small-Large isolated
## 1053 3 12 1006 2|11 low Small-Large isolated
## 1054 3 12 1006 2|11 low Small-Large isolated
## 1055 3 12 1006 2|11 low Small-Large isolated
## 1056 3 12 1006 2|11 low Small-Large isolated
## 1057 4 16 1006 2|11 low Small-Large isolated
## 1058 4 16 1006 2|11 low Small-Large isolated
## 1059 4 16 1006 2|11 low Small-Large isolated
## 1060 4 16 1006 2|11 low Small-Large isolated
## 1061 4 16 1006 2|11 low Small-Large isolated
## 1062 4 16 1006 2|11 low Small-Large isolated
## 1063 4 16 1006 2|11 low Small-Large isolated
## 1064 4 16 1006 2|11 low Small-Large isolated
## 1065 4 16 1006 2|11 low Small-Large isolated
## 1066 4 16 1006 2|11 low Small-Large isolated
## 1067 4 16 1006 2|11 low Small-Large isolated
## 1068 4 16 1006 2|11 low Small-Large isolated
## 1069 4 16 1006 2|11 low Small-Large isolated
## 1070 4 16 1006 2|11 low Small-Large isolated
## 1071 4 16 1006 2|11 low Small-Large isolated
## 1072 4 16 1006 2|11 low Small-Large isolated
## 1073 4 16 1006 2|11 low Small-Large isolated
## 1074 4 16 1006 2|11 low Small-Large isolated
## 1075 4 16 1006 2|11 low Small-Large isolated
## 1076 4 16 1006 2|11 low Small-Large isolated
## 1077 4 16 1006 2|11 low Small-Large isolated
## 1078 4 16 1006 2|11 low Small-Large isolated
## 1079 4 16 1006 2|11 low Small-Large isolated
## 1080 4 16 1006 2|11 low Small-Large isolated
## 1081 5 20 1006 2|11 low Small-Large isolated
## 1082 5 20 1006 2|11 low Small-Large isolated
## 1083 5 20 1006 2|11 low Small-Large isolated
## 1084 5 20 1006 2|11 low Small-Large isolated
## 1085 5 20 1006 2|11 low Small-Large isolated
## 1086 5 20 1006 2|11 low Small-Large isolated
## 1087 5 20 1006 2|11 low Small-Large isolated
## 1088 5 20 1006 2|11 low Small-Large isolated
## 1089 5 20 1006 2|11 low Small-Large isolated
## 1090 5 20 1006 2|11 low Small-Large isolated
## 1091 5 20 1006 2|11 low Small-Large isolated
## 1092 5 20 1006 2|11 low Small-Large isolated
## 1093 5 20 1006 2|11 low Small-Large isolated
## 1094 5 20 1006 2|11 low Small-Large isolated
## 1095 5 20 1006 2|11 low Small-Large isolated
## 1096 5 20 1006 2|11 low Small-Large isolated
## 1097 5 20 1006 2|11 low Small-Large isolated
## 1098 5 20 1006 2|11 low Small-Large isolated
## 1099 5 20 1006 2|11 low Small-Large isolated
## 1100 5 20 1006 2|11 low Small-Large isolated
## 1101 5 20 1006 2|11 low Small-Large isolated
## 1102 5 20 1006 2|11 low Small-Large isolated
## 1103 5 20 1006 2|11 low Small-Large isolated
## 1104 5 20 1006 2|11 low Small-Large isolated
## 1105 6 24 1006 2|11 low Small-Large isolated
## 1106 6 24 1006 2|11 low Small-Large isolated
## 1107 6 24 1006 2|11 low Small-Large isolated
## 1108 6 24 1006 2|11 low Small-Large isolated
## 1109 6 24 1006 2|11 low Small-Large isolated
## 1110 6 24 1006 2|11 low Small-Large isolated
## 1111 6 24 1006 2|11 low Small-Large isolated
## 1112 6 24 1006 2|11 low Small-Large isolated
## 1113 6 24 1006 2|11 low Small-Large isolated
## 1114 6 24 1006 2|11 low Small-Large isolated
## 1115 6 24 1006 2|11 low Small-Large isolated
## 1116 6 24 1006 2|11 low Small-Large isolated
## 1117 6 24 1006 2|11 low Small-Large isolated
## 1118 6 24 1006 2|11 low Small-Large isolated
## 1119 6 24 1006 2|11 low Small-Large isolated
## 1120 6 24 1006 2|11 low Small-Large isolated
## 1121 6 24 1006 2|11 low Small-Large isolated
## 1122 6 24 1006 2|11 low Small-Large isolated
## 1123 6 24 1006 2|11 low Small-Large isolated
## 1124 6 24 1006 2|11 low Small-Large isolated
## 1125 6 24 1006 2|11 low Small-Large isolated
## 1126 6 24 1006 2|11 low Small-Large isolated
## 1127 6 24 1006 2|11 low Small-Large isolated
## 1128 6 24 1006 2|11 low Small-Large isolated
## 1129 7 28 1006 2|11 low Small-Large isolated
## 1130 7 28 1006 2|11 low Small-Large isolated
## 1131 7 28 1006 2|11 low Small-Large isolated
## 1132 7 28 1006 2|11 low Small-Large isolated
## 1133 7 28 1006 2|11 low Small-Large isolated
## 1134 7 28 1006 2|11 low Small-Large isolated
## 1135 7 28 1006 2|11 low Small-Large isolated
## 1136 7 28 1006 2|11 low Small-Large isolated
## 1137 7 28 1006 2|11 low Small-Large isolated
## 1138 7 28 1006 2|11 low Small-Large isolated
## 1139 7 28 1006 2|11 low Small-Large isolated
## 1140 7 28 1006 2|11 low Small-Large isolated
## 1141 7 28 1006 2|11 low Small-Large isolated
## 1142 7 28 1006 2|11 low Small-Large isolated
## 1143 7 28 1006 2|11 low Small-Large isolated
## 1144 7 28 1006 2|11 low Small-Large isolated
## 1145 7 28 1006 2|11 low Small-Large isolated
## 1146 7 28 1006 2|11 low Small-Large isolated
## 1147 7 28 1006 2|11 low Small-Large isolated
## 1148 7 28 1006 2|11 low Small-Large isolated
## 1149 7 28 1006 2|11 low Small-Large isolated
## 1150 7 28 1006 2|11 low Small-Large isolated
## 1151 7 28 1006 2|11 low Small-Large isolated
## 1152 7 28 1006 2|11 low Small-Large isolated
## 1153 0 0 1007 2|12 low Small-Large isolated
## 1154 0 0 1007 2|12 low Small-Large isolated
## 1155 0 0 1007 2|12 low Small-Large isolated
## 1156 0 0 1007 2|12 low Small-Large isolated
## 1157 0 0 1007 2|12 low Small-Large isolated
## 1158 0 0 1007 2|12 low Small-Large isolated
## 1159 0 0 1007 2|12 low Small-Large isolated
## 1160 0 0 1007 2|12 low Small-Large isolated
## 1161 0 0 1007 2|12 low Small-Large isolated
## 1162 0 0 1007 2|12 low Small-Large isolated
## 1163 0 0 1007 2|12 low Small-Large isolated
## 1164 0 0 1007 2|12 low Small-Large isolated
## 1165 0 0 1007 2|12 low Small-Large isolated
## 1166 0 0 1007 2|12 low Small-Large isolated
## 1167 0 0 1007 2|12 low Small-Large isolated
## 1168 0 0 1007 2|12 low Small-Large isolated
## 1169 0 0 1007 2|12 low Small-Large isolated
## 1170 0 0 1007 2|12 low Small-Large isolated
## 1171 0 0 1007 2|12 low Small-Large isolated
## 1172 0 0 1007 2|12 low Small-Large isolated
## 1173 0 0 1007 2|12 low Small-Large isolated
## 1174 0 0 1007 2|12 low Small-Large isolated
## 1175 0 0 1007 2|12 low Small-Large isolated
## 1176 0 0 1007 2|12 low Small-Large isolated
## 1177 1 4 1007 2|12 low Small-Large isolated
## 1178 1 4 1007 2|12 low Small-Large isolated
## 1179 1 4 1007 2|12 low Small-Large isolated
## 1180 1 4 1007 2|12 low Small-Large isolated
## 1181 1 4 1007 2|12 low Small-Large isolated
## 1182 1 4 1007 2|12 low Small-Large isolated
## 1183 1 4 1007 2|12 low Small-Large isolated
## 1184 1 4 1007 2|12 low Small-Large isolated
## 1185 1 4 1007 2|12 low Small-Large isolated
## 1186 1 4 1007 2|12 low Small-Large isolated
## 1187 1 4 1007 2|12 low Small-Large isolated
## 1188 1 4 1007 2|12 low Small-Large isolated
## 1189 1 4 1007 2|12 low Small-Large isolated
## 1190 1 4 1007 2|12 low Small-Large isolated
## 1191 1 4 1007 2|12 low Small-Large isolated
## 1192 1 4 1007 2|12 low Small-Large isolated
## 1193 1 4 1007 2|12 low Small-Large isolated
## 1194 1 4 1007 2|12 low Small-Large isolated
## 1195 1 4 1007 2|12 low Small-Large isolated
## 1196 1 4 1007 2|12 low Small-Large isolated
## 1197 1 4 1007 2|12 low Small-Large isolated
## 1198 1 4 1007 2|12 low Small-Large isolated
## 1199 1 4 1007 2|12 low Small-Large isolated
## 1200 1 4 1007 2|12 low Small-Large isolated
## 1201 2 8 1007 2|12 low Small-Large isolated
## 1202 2 8 1007 2|12 low Small-Large isolated
## 1203 2 8 1007 2|12 low Small-Large isolated
## 1204 2 8 1007 2|12 low Small-Large isolated
## 1205 2 8 1007 2|12 low Small-Large isolated
## 1206 2 8 1007 2|12 low Small-Large isolated
## 1207 2 8 1007 2|12 low Small-Large isolated
## 1208 2 8 1007 2|12 low Small-Large isolated
## 1209 2 8 1007 2|12 low Small-Large isolated
## 1210 2 8 1007 2|12 low Small-Large isolated
## 1211 2 8 1007 2|12 low Small-Large isolated
## 1212 2 8 1007 2|12 low Small-Large isolated
## 1213 2 8 1007 2|12 low Small-Large isolated
## 1214 2 8 1007 2|12 low Small-Large isolated
## 1215 2 8 1007 2|12 low Small-Large isolated
## 1216 2 8 1007 2|12 low Small-Large isolated
## 1217 2 8 1007 2|12 low Small-Large isolated
## 1218 2 8 1007 2|12 low Small-Large isolated
## 1219 2 8 1007 2|12 low Small-Large isolated
## 1220 2 8 1007 2|12 low Small-Large isolated
## 1221 2 8 1007 2|12 low Small-Large isolated
## 1222 2 8 1007 2|12 low Small-Large isolated
## 1223 2 8 1007 2|12 low Small-Large isolated
## 1224 2 8 1007 2|12 low Small-Large isolated
## 1225 3 12 1007 2|12 low Small-Large isolated
## 1226 3 12 1007 2|12 low Small-Large isolated
## 1227 3 12 1007 2|12 low Small-Large isolated
## 1228 3 12 1007 2|12 low Small-Large isolated
## 1229 3 12 1007 2|12 low Small-Large isolated
## 1230 3 12 1007 2|12 low Small-Large isolated
## 1231 3 12 1007 2|12 low Small-Large isolated
## 1232 3 12 1007 2|12 low Small-Large isolated
## 1233 3 12 1007 2|12 low Small-Large isolated
## 1234 3 12 1007 2|12 low Small-Large isolated
## 1235 3 12 1007 2|12 low Small-Large isolated
## 1236 3 12 1007 2|12 low Small-Large isolated
## 1237 3 12 1007 2|12 low Small-Large isolated
## 1238 3 12 1007 2|12 low Small-Large isolated
## 1239 3 12 1007 2|12 low Small-Large isolated
## 1240 3 12 1007 2|12 low Small-Large isolated
## 1241 3 12 1007 2|12 low Small-Large isolated
## 1242 3 12 1007 2|12 low Small-Large isolated
## 1243 3 12 1007 2|12 low Small-Large isolated
## 1244 3 12 1007 2|12 low Small-Large isolated
## 1245 3 12 1007 2|12 low Small-Large isolated
## 1246 3 12 1007 2|12 low Small-Large isolated
## 1247 3 12 1007 2|12 low Small-Large isolated
## 1248 3 12 1007 2|12 low Small-Large isolated
## 1249 4 16 1007 2|12 low Small-Large isolated
## 1250 4 16 1007 2|12 low Small-Large isolated
## 1251 4 16 1007 2|12 low Small-Large isolated
## 1252 4 16 1007 2|12 low Small-Large isolated
## 1253 4 16 1007 2|12 low Small-Large isolated
## 1254 4 16 1007 2|12 low Small-Large isolated
## 1255 4 16 1007 2|12 low Small-Large isolated
## 1256 4 16 1007 2|12 low Small-Large isolated
## 1257 4 16 1007 2|12 low Small-Large isolated
## 1258 4 16 1007 2|12 low Small-Large isolated
## 1259 4 16 1007 2|12 low Small-Large isolated
## 1260 4 16 1007 2|12 low Small-Large isolated
## 1261 4 16 1007 2|12 low Small-Large isolated
## 1262 4 16 1007 2|12 low Small-Large isolated
## 1263 4 16 1007 2|12 low Small-Large isolated
## 1264 4 16 1007 2|12 low Small-Large isolated
## 1265 4 16 1007 2|12 low Small-Large isolated
## 1266 4 16 1007 2|12 low Small-Large isolated
## 1267 4 16 1007 2|12 low Small-Large isolated
## 1268 4 16 1007 2|12 low Small-Large isolated
## 1269 4 16 1007 2|12 low Small-Large isolated
## 1270 4 16 1007 2|12 low Small-Large isolated
## 1271 4 16 1007 2|12 low Small-Large isolated
## 1272 4 16 1007 2|12 low Small-Large isolated
## 1273 5 20 1007 2|12 low Small-Large isolated
## 1274 5 20 1007 2|12 low Small-Large isolated
## 1275 5 20 1007 2|12 low Small-Large isolated
## 1276 5 20 1007 2|12 low Small-Large isolated
## 1277 5 20 1007 2|12 low Small-Large isolated
## 1278 5 20 1007 2|12 low Small-Large isolated
## 1279 5 20 1007 2|12 low Small-Large isolated
## 1280 5 20 1007 2|12 low Small-Large isolated
## 1281 5 20 1007 2|12 low Small-Large isolated
## 1282 5 20 1007 2|12 low Small-Large isolated
## 1283 5 20 1007 2|12 low Small-Large isolated
## 1284 5 20 1007 2|12 low Small-Large isolated
## 1285 5 20 1007 2|12 low Small-Large isolated
## 1286 5 20 1007 2|12 low Small-Large isolated
## 1287 5 20 1007 2|12 low Small-Large isolated
## 1288 5 20 1007 2|12 low Small-Large isolated
## 1289 5 20 1007 2|12 low Small-Large isolated
## 1290 5 20 1007 2|12 low Small-Large isolated
## 1291 5 20 1007 2|12 low Small-Large isolated
## 1292 5 20 1007 2|12 low Small-Large isolated
## 1293 5 20 1007 2|12 low Small-Large isolated
## 1294 5 20 1007 2|12 low Small-Large isolated
## 1295 5 20 1007 2|12 low Small-Large isolated
## 1296 5 20 1007 2|12 low Small-Large isolated
## 1297 6 24 1007 2|12 low Small-Large isolated
## 1298 6 24 1007 2|12 low Small-Large isolated
## 1299 6 24 1007 2|12 low Small-Large isolated
## 1300 6 24 1007 2|12 low Small-Large isolated
## 1301 6 24 1007 2|12 low Small-Large isolated
## 1302 6 24 1007 2|12 low Small-Large isolated
## 1303 6 24 1007 2|12 low Small-Large isolated
## 1304 6 24 1007 2|12 low Small-Large isolated
## 1305 6 24 1007 2|12 low Small-Large isolated
## 1306 6 24 1007 2|12 low Small-Large isolated
## 1307 6 24 1007 2|12 low Small-Large isolated
## 1308 6 24 1007 2|12 low Small-Large isolated
## 1309 6 24 1007 2|12 low Small-Large isolated
## 1310 6 24 1007 2|12 low Small-Large isolated
## 1311 6 24 1007 2|12 low Small-Large isolated
## 1312 6 24 1007 2|12 low Small-Large isolated
## 1313 6 24 1007 2|12 low Small-Large isolated
## 1314 6 24 1007 2|12 low Small-Large isolated
## 1315 6 24 1007 2|12 low Small-Large isolated
## 1316 6 24 1007 2|12 low Small-Large isolated
## 1317 6 24 1007 2|12 low Small-Large isolated
## 1318 6 24 1007 2|12 low Small-Large isolated
## 1319 6 24 1007 2|12 low Small-Large isolated
## 1320 6 24 1007 2|12 low Small-Large isolated
## 1321 7 28 1007 2|12 low Small-Large isolated
## 1322 7 28 1007 2|12 low Small-Large isolated
## 1323 7 28 1007 2|12 low Small-Large isolated
## 1324 7 28 1007 2|12 low Small-Large isolated
## 1325 7 28 1007 2|12 low Small-Large isolated
## 1326 7 28 1007 2|12 low Small-Large isolated
## 1327 7 28 1007 2|12 low Small-Large isolated
## 1328 7 28 1007 2|12 low Small-Large isolated
## 1329 7 28 1007 2|12 low Small-Large isolated
## 1330 7 28 1007 2|12 low Small-Large isolated
## 1331 7 28 1007 2|12 low Small-Large isolated
## 1332 7 28 1007 2|12 low Small-Large isolated
## 1333 7 28 1007 2|12 low Small-Large isolated
## 1334 7 28 1007 2|12 low Small-Large isolated
## 1335 7 28 1007 2|12 low Small-Large isolated
## 1336 7 28 1007 2|12 low Small-Large isolated
## 1337 7 28 1007 2|12 low Small-Large isolated
## 1338 7 28 1007 2|12 low Small-Large isolated
## 1339 7 28 1007 2|12 low Small-Large isolated
## 1340 7 28 1007 2|12 low Small-Large isolated
## 1341 7 28 1007 2|12 low Small-Large isolated
## 1342 7 28 1007 2|12 low Small-Large isolated
## 1343 7 28 1007 2|12 low Small-Large isolated
## 1344 7 28 1007 2|12 low Small-Large isolated
## 1345 0 0 1008 2|13 low Small-Large isolated
## 1346 0 0 1008 2|13 low Small-Large isolated
## 1347 0 0 1008 2|13 low Small-Large isolated
## 1348 0 0 1008 2|13 low Small-Large isolated
## 1349 0 0 1008 2|13 low Small-Large isolated
## 1350 0 0 1008 2|13 low Small-Large isolated
## 1351 0 0 1008 2|13 low Small-Large isolated
## 1352 0 0 1008 2|13 low Small-Large isolated
## 1353 0 0 1008 2|13 low Small-Large isolated
## 1354 0 0 1008 2|13 low Small-Large isolated
## 1355 0 0 1008 2|13 low Small-Large isolated
## 1356 0 0 1008 2|13 low Small-Large isolated
## 1357 0 0 1008 2|13 low Small-Large isolated
## 1358 0 0 1008 2|13 low Small-Large isolated
## 1359 0 0 1008 2|13 low Small-Large isolated
## 1360 0 0 1008 2|13 low Small-Large isolated
## 1361 0 0 1008 2|13 low Small-Large isolated
## 1362 0 0 1008 2|13 low Small-Large isolated
## 1363 0 0 1008 2|13 low Small-Large isolated
## 1364 0 0 1008 2|13 low Small-Large isolated
## 1365 0 0 1008 2|13 low Small-Large isolated
## 1366 0 0 1008 2|13 low Small-Large isolated
## 1367 0 0 1008 2|13 low Small-Large isolated
## 1368 0 0 1008 2|13 low Small-Large isolated
## 1369 1 4 1008 2|13 low Small-Large isolated
## 1370 1 4 1008 2|13 low Small-Large isolated
## 1371 1 4 1008 2|13 low Small-Large isolated
## 1372 1 4 1008 2|13 low Small-Large isolated
## 1373 1 4 1008 2|13 low Small-Large isolated
## 1374 1 4 1008 2|13 low Small-Large isolated
## 1375 1 4 1008 2|13 low Small-Large isolated
## 1376 1 4 1008 2|13 low Small-Large isolated
## 1377 1 4 1008 2|13 low Small-Large isolated
## 1378 1 4 1008 2|13 low Small-Large isolated
## 1379 1 4 1008 2|13 low Small-Large isolated
## 1380 1 4 1008 2|13 low Small-Large isolated
## 1381 1 4 1008 2|13 low Small-Large isolated
## 1382 1 4 1008 2|13 low Small-Large isolated
## 1383 1 4 1008 2|13 low Small-Large isolated
## 1384 1 4 1008 2|13 low Small-Large isolated
## 1385 1 4 1008 2|13 low Small-Large isolated
## 1386 1 4 1008 2|13 low Small-Large isolated
## 1387 1 4 1008 2|13 low Small-Large isolated
## 1388 1 4 1008 2|13 low Small-Large isolated
## 1389 1 4 1008 2|13 low Small-Large isolated
## 1390 1 4 1008 2|13 low Small-Large isolated
## 1391 1 4 1008 2|13 low Small-Large isolated
## 1392 1 4 1008 2|13 low Small-Large isolated
## 1393 2 8 1008 2|13 low Small-Large isolated
## 1394 2 8 1008 2|13 low Small-Large isolated
## 1395 2 8 1008 2|13 low Small-Large isolated
## 1396 2 8 1008 2|13 low Small-Large isolated
## 1397 2 8 1008 2|13 low Small-Large isolated
## 1398 2 8 1008 2|13 low Small-Large isolated
## 1399 2 8 1008 2|13 low Small-Large isolated
## 1400 2 8 1008 2|13 low Small-Large isolated
## 1401 2 8 1008 2|13 low Small-Large isolated
## 1402 2 8 1008 2|13 low Small-Large isolated
## 1403 2 8 1008 2|13 low Small-Large isolated
## 1404 2 8 1008 2|13 low Small-Large isolated
## 1405 2 8 1008 2|13 low Small-Large isolated
## 1406 2 8 1008 2|13 low Small-Large isolated
## 1407 2 8 1008 2|13 low Small-Large isolated
## 1408 2 8 1008 2|13 low Small-Large isolated
## 1409 2 8 1008 2|13 low Small-Large isolated
## 1410 2 8 1008 2|13 low Small-Large isolated
## 1411 2 8 1008 2|13 low Small-Large isolated
## 1412 2 8 1008 2|13 low Small-Large isolated
## 1413 2 8 1008 2|13 low Small-Large isolated
## 1414 2 8 1008 2|13 low Small-Large isolated
## 1415 2 8 1008 2|13 low Small-Large isolated
## 1416 2 8 1008 2|13 low Small-Large isolated
## 1417 3 12 1008 2|13 low Small-Large isolated
## 1418 3 12 1008 2|13 low Small-Large isolated
## 1419 3 12 1008 2|13 low Small-Large isolated
## 1420 3 12 1008 2|13 low Small-Large isolated
## 1421 3 12 1008 2|13 low Small-Large isolated
## 1422 3 12 1008 2|13 low Small-Large isolated
## 1423 3 12 1008 2|13 low Small-Large isolated
## 1424 3 12 1008 2|13 low Small-Large isolated
## 1425 3 12 1008 2|13 low Small-Large isolated
## 1426 3 12 1008 2|13 low Small-Large isolated
## 1427 3 12 1008 2|13 low Small-Large isolated
## 1428 3 12 1008 2|13 low Small-Large isolated
## 1429 3 12 1008 2|13 low Small-Large isolated
## 1430 3 12 1008 2|13 low Small-Large isolated
## 1431 3 12 1008 2|13 low Small-Large isolated
## 1432 3 12 1008 2|13 low Small-Large isolated
## 1433 3 12 1008 2|13 low Small-Large isolated
## 1434 3 12 1008 2|13 low Small-Large isolated
## 1435 3 12 1008 2|13 low Small-Large isolated
## 1436 3 12 1008 2|13 low Small-Large isolated
## 1437 3 12 1008 2|13 low Small-Large isolated
## 1438 3 12 1008 2|13 low Small-Large isolated
## 1439 3 12 1008 2|13 low Small-Large isolated
## 1440 3 12 1008 2|13 low Small-Large isolated
## 1441 4 16 1008 2|13 low Small-Large isolated
## 1442 4 16 1008 2|13 low Small-Large isolated
## 1443 4 16 1008 2|13 low Small-Large isolated
## 1444 4 16 1008 2|13 low Small-Large isolated
## 1445 4 16 1008 2|13 low Small-Large isolated
## 1446 4 16 1008 2|13 low Small-Large isolated
## 1447 4 16 1008 2|13 low Small-Large isolated
## 1448 4 16 1008 2|13 low Small-Large isolated
## 1449 4 16 1008 2|13 low Small-Large isolated
## 1450 4 16 1008 2|13 low Small-Large isolated
## 1451 4 16 1008 2|13 low Small-Large isolated
## 1452 4 16 1008 2|13 low Small-Large isolated
## 1453 4 16 1008 2|13 low Small-Large isolated
## 1454 4 16 1008 2|13 low Small-Large isolated
## 1455 4 16 1008 2|13 low Small-Large isolated
## 1456 4 16 1008 2|13 low Small-Large isolated
## 1457 4 16 1008 2|13 low Small-Large isolated
## 1458 4 16 1008 2|13 low Small-Large isolated
## 1459 4 16 1008 2|13 low Small-Large isolated
## 1460 4 16 1008 2|13 low Small-Large isolated
## 1461 4 16 1008 2|13 low Small-Large isolated
## 1462 4 16 1008 2|13 low Small-Large isolated
## 1463 4 16 1008 2|13 low Small-Large isolated
## 1464 4 16 1008 2|13 low Small-Large isolated
## 1465 5 20 1008 2|13 low Small-Large isolated
## 1466 5 20 1008 2|13 low Small-Large isolated
## 1467 5 20 1008 2|13 low Small-Large isolated
## 1468 5 20 1008 2|13 low Small-Large isolated
## 1469 5 20 1008 2|13 low Small-Large isolated
## 1470 5 20 1008 2|13 low Small-Large isolated
## 1471 5 20 1008 2|13 low Small-Large isolated
## 1472 5 20 1008 2|13 low Small-Large isolated
## 1473 5 20 1008 2|13 low Small-Large isolated
## 1474 5 20 1008 2|13 low Small-Large isolated
## 1475 5 20 1008 2|13 low Small-Large isolated
## 1476 5 20 1008 2|13 low Small-Large isolated
## 1477 5 20 1008 2|13 low Small-Large isolated
## 1478 5 20 1008 2|13 low Small-Large isolated
## 1479 5 20 1008 2|13 low Small-Large isolated
## 1480 5 20 1008 2|13 low Small-Large isolated
## 1481 5 20 1008 2|13 low Small-Large isolated
## 1482 5 20 1008 2|13 low Small-Large isolated
## 1483 5 20 1008 2|13 low Small-Large isolated
## 1484 5 20 1008 2|13 low Small-Large isolated
## 1485 5 20 1008 2|13 low Small-Large isolated
## 1486 5 20 1008 2|13 low Small-Large isolated
## 1487 5 20 1008 2|13 low Small-Large isolated
## 1488 5 20 1008 2|13 low Small-Large isolated
## 1489 6 24 1008 2|13 low Small-Large isolated
## 1490 6 24 1008 2|13 low Small-Large isolated
## 1491 6 24 1008 2|13 low Small-Large isolated
## 1492 6 24 1008 2|13 low Small-Large isolated
## 1493 6 24 1008 2|13 low Small-Large isolated
## 1494 6 24 1008 2|13 low Small-Large isolated
## 1495 6 24 1008 2|13 low Small-Large isolated
## 1496 6 24 1008 2|13 low Small-Large isolated
## 1497 6 24 1008 2|13 low Small-Large isolated
## 1498 6 24 1008 2|13 low Small-Large isolated
## 1499 6 24 1008 2|13 low Small-Large isolated
## 1500 6 24 1008 2|13 low Small-Large isolated
## 1501 6 24 1008 2|13 low Small-Large isolated
## 1502 6 24 1008 2|13 low Small-Large isolated
## 1503 6 24 1008 2|13 low Small-Large isolated
## 1504 6 24 1008 2|13 low Small-Large isolated
## 1505 6 24 1008 2|13 low Small-Large isolated
## 1506 6 24 1008 2|13 low Small-Large isolated
## 1507 6 24 1008 2|13 low Small-Large isolated
## 1508 6 24 1008 2|13 low Small-Large isolated
## 1509 6 24 1008 2|13 low Small-Large isolated
## 1510 6 24 1008 2|13 low Small-Large isolated
## 1511 6 24 1008 2|13 low Small-Large isolated
## 1512 6 24 1008 2|13 low Small-Large isolated
## 1513 7 28 1008 2|13 low Small-Large isolated
## 1514 7 28 1008 2|13 low Small-Large isolated
## 1515 7 28 1008 2|13 low Small-Large isolated
## 1516 7 28 1008 2|13 low Small-Large isolated
## 1517 7 28 1008 2|13 low Small-Large isolated
## 1518 7 28 1008 2|13 low Small-Large isolated
## 1519 7 28 1008 2|13 low Small-Large isolated
## 1520 7 28 1008 2|13 low Small-Large isolated
## 1521 7 28 1008 2|13 low Small-Large isolated
## 1522 7 28 1008 2|13 low Small-Large isolated
## 1523 7 28 1008 2|13 low Small-Large isolated
## 1524 7 28 1008 2|13 low Small-Large isolated
## 1525 7 28 1008 2|13 low Small-Large isolated
## 1526 7 28 1008 2|13 low Small-Large isolated
## 1527 7 28 1008 2|13 low Small-Large isolated
## 1528 7 28 1008 2|13 low Small-Large isolated
## 1529 7 28 1008 2|13 low Small-Large isolated
## 1530 7 28 1008 2|13 low Small-Large isolated
## 1531 7 28 1008 2|13 low Small-Large isolated
## 1532 7 28 1008 2|13 low Small-Large isolated
## 1533 7 28 1008 2|13 low Small-Large isolated
## 1534 7 28 1008 2|13 low Small-Large isolated
## 1535 7 28 1008 2|13 low Small-Large isolated
## 1536 7 28 1008 2|13 low Small-Large isolated
## 1537 0 0 1009 2|14 low Small-Large isolated
## 1538 0 0 1009 2|14 low Small-Large isolated
## 1539 0 0 1009 2|14 low Small-Large isolated
## 1540 0 0 1009 2|14 low Small-Large isolated
## 1541 0 0 1009 2|14 low Small-Large isolated
## 1542 0 0 1009 2|14 low Small-Large isolated
## 1543 0 0 1009 2|14 low Small-Large isolated
## 1544 0 0 1009 2|14 low Small-Large isolated
## 1545 0 0 1009 2|14 low Small-Large isolated
## 1546 0 0 1009 2|14 low Small-Large isolated
## 1547 0 0 1009 2|14 low Small-Large isolated
## 1548 0 0 1009 2|14 low Small-Large isolated
## 1549 0 0 1009 2|14 low Small-Large isolated
## 1550 0 0 1009 2|14 low Small-Large isolated
## 1551 0 0 1009 2|14 low Small-Large isolated
## 1552 0 0 1009 2|14 low Small-Large isolated
## 1553 0 0 1009 2|14 low Small-Large isolated
## 1554 0 0 1009 2|14 low Small-Large isolated
## 1555 0 0 1009 2|14 low Small-Large isolated
## 1556 0 0 1009 2|14 low Small-Large isolated
## 1557 0 0 1009 2|14 low Small-Large isolated
## 1558 0 0 1009 2|14 low Small-Large isolated
## 1559 0 0 1009 2|14 low Small-Large isolated
## 1560 0 0 1009 2|14 low Small-Large isolated
## 1561 1 4 1009 2|14 low Small-Large isolated
## 1562 1 4 1009 2|14 low Small-Large isolated
## 1563 1 4 1009 2|14 low Small-Large isolated
## 1564 1 4 1009 2|14 low Small-Large isolated
## 1565 1 4 1009 2|14 low Small-Large isolated
## 1566 1 4 1009 2|14 low Small-Large isolated
## 1567 1 4 1009 2|14 low Small-Large isolated
## 1568 1 4 1009 2|14 low Small-Large isolated
## 1569 1 4 1009 2|14 low Small-Large isolated
## 1570 1 4 1009 2|14 low Small-Large isolated
## 1571 1 4 1009 2|14 low Small-Large isolated
## 1572 1 4 1009 2|14 low Small-Large isolated
## 1573 1 4 1009 2|14 low Small-Large isolated
## 1574 1 4 1009 2|14 low Small-Large isolated
## 1575 1 4 1009 2|14 low Small-Large isolated
## 1576 1 4 1009 2|14 low Small-Large isolated
## 1577 1 4 1009 2|14 low Small-Large isolated
## 1578 1 4 1009 2|14 low Small-Large isolated
## 1579 1 4 1009 2|14 low Small-Large isolated
## 1580 1 4 1009 2|14 low Small-Large isolated
## 1581 1 4 1009 2|14 low Small-Large isolated
## 1582 1 4 1009 2|14 low Small-Large isolated
## 1583 1 4 1009 2|14 low Small-Large isolated
## 1584 1 4 1009 2|14 low Small-Large isolated
## 1585 2 8 1009 2|14 low Small-Large isolated
## 1586 2 8 1009 2|14 low Small-Large isolated
## 1587 2 8 1009 2|14 low Small-Large isolated
## 1588 2 8 1009 2|14 low Small-Large isolated
## 1589 2 8 1009 2|14 low Small-Large isolated
## 1590 2 8 1009 2|14 low Small-Large isolated
## 1591 2 8 1009 2|14 low Small-Large isolated
## 1592 2 8 1009 2|14 low Small-Large isolated
## 1593 2 8 1009 2|14 low Small-Large isolated
## 1594 2 8 1009 2|14 low Small-Large isolated
## 1595 2 8 1009 2|14 low Small-Large isolated
## 1596 2 8 1009 2|14 low Small-Large isolated
## 1597 2 8 1009 2|14 low Small-Large isolated
## 1598 2 8 1009 2|14 low Small-Large isolated
## 1599 2 8 1009 2|14 low Small-Large isolated
## 1600 2 8 1009 2|14 low Small-Large isolated
## 1601 2 8 1009 2|14 low Small-Large isolated
## 1602 2 8 1009 2|14 low Small-Large isolated
## 1603 2 8 1009 2|14 low Small-Large isolated
## 1604 2 8 1009 2|14 low Small-Large isolated
## 1605 2 8 1009 2|14 low Small-Large isolated
## 1606 2 8 1009 2|14 low Small-Large isolated
## 1607 2 8 1009 2|14 low Small-Large isolated
## 1608 2 8 1009 2|14 low Small-Large isolated
## 1609 3 12 1009 2|14 low Small-Large isolated
## 1610 3 12 1009 2|14 low Small-Large isolated
## 1611 3 12 1009 2|14 low Small-Large isolated
## 1612 3 12 1009 2|14 low Small-Large isolated
## 1613 3 12 1009 2|14 low Small-Large isolated
## 1614 3 12 1009 2|14 low Small-Large isolated
## 1615 3 12 1009 2|14 low Small-Large isolated
## 1616 3 12 1009 2|14 low Small-Large isolated
## 1617 3 12 1009 2|14 low Small-Large isolated
## 1618 3 12 1009 2|14 low Small-Large isolated
## 1619 3 12 1009 2|14 low Small-Large isolated
## 1620 3 12 1009 2|14 low Small-Large isolated
## 1621 3 12 1009 2|14 low Small-Large isolated
## 1622 3 12 1009 2|14 low Small-Large isolated
## 1623 3 12 1009 2|14 low Small-Large isolated
## 1624 3 12 1009 2|14 low Small-Large isolated
## 1625 3 12 1009 2|14 low Small-Large isolated
## 1626 3 12 1009 2|14 low Small-Large isolated
## 1627 3 12 1009 2|14 low Small-Large isolated
## 1628 3 12 1009 2|14 low Small-Large isolated
## 1629 3 12 1009 2|14 low Small-Large isolated
## 1630 3 12 1009 2|14 low Small-Large isolated
## 1631 3 12 1009 2|14 low Small-Large isolated
## 1632 3 12 1009 2|14 low Small-Large isolated
## 1633 4 16 1009 2|14 low Small-Large isolated
## 1634 4 16 1009 2|14 low Small-Large isolated
## 1635 4 16 1009 2|14 low Small-Large isolated
## 1636 4 16 1009 2|14 low Small-Large isolated
## 1637 4 16 1009 2|14 low Small-Large isolated
## 1638 4 16 1009 2|14 low Small-Large isolated
## 1639 4 16 1009 2|14 low Small-Large isolated
## 1640 4 16 1009 2|14 low Small-Large isolated
## 1641 4 16 1009 2|14 low Small-Large isolated
## 1642 4 16 1009 2|14 low Small-Large isolated
## 1643 4 16 1009 2|14 low Small-Large isolated
## 1644 4 16 1009 2|14 low Small-Large isolated
## 1645 4 16 1009 2|14 low Small-Large isolated
## 1646 4 16 1009 2|14 low Small-Large isolated
## 1647 4 16 1009 2|14 low Small-Large isolated
## 1648 4 16 1009 2|14 low Small-Large isolated
## 1649 4 16 1009 2|14 low Small-Large isolated
## 1650 4 16 1009 2|14 low Small-Large isolated
## 1651 4 16 1009 2|14 low Small-Large isolated
## 1652 4 16 1009 2|14 low Small-Large isolated
## 1653 4 16 1009 2|14 low Small-Large isolated
## 1654 4 16 1009 2|14 low Small-Large isolated
## 1655 4 16 1009 2|14 low Small-Large isolated
## 1656 4 16 1009 2|14 low Small-Large isolated
## 1657 5 20 1009 2|14 low Small-Large isolated
## 1658 5 20 1009 2|14 low Small-Large isolated
## 1659 5 20 1009 2|14 low Small-Large isolated
## 1660 5 20 1009 2|14 low Small-Large isolated
## 1661 5 20 1009 2|14 low Small-Large isolated
## 1662 5 20 1009 2|14 low Small-Large isolated
## 1663 5 20 1009 2|14 low Small-Large isolated
## 1664 5 20 1009 2|14 low Small-Large isolated
## 1665 5 20 1009 2|14 low Small-Large isolated
## 1666 5 20 1009 2|14 low Small-Large isolated
## 1667 5 20 1009 2|14 low Small-Large isolated
## 1668 5 20 1009 2|14 low Small-Large isolated
## 1669 5 20 1009 2|14 low Small-Large isolated
## 1670 5 20 1009 2|14 low Small-Large isolated
## 1671 5 20 1009 2|14 low Small-Large isolated
## 1672 5 20 1009 2|14 low Small-Large isolated
## 1673 5 20 1009 2|14 low Small-Large isolated
## 1674 5 20 1009 2|14 low Small-Large isolated
## 1675 5 20 1009 2|14 low Small-Large isolated
## 1676 5 20 1009 2|14 low Small-Large isolated
## 1677 5 20 1009 2|14 low Small-Large isolated
## 1678 5 20 1009 2|14 low Small-Large isolated
## 1679 5 20 1009 2|14 low Small-Large isolated
## 1680 5 20 1009 2|14 low Small-Large isolated
## 1681 6 24 1009 2|14 low Small-Large isolated
## 1682 6 24 1009 2|14 low Small-Large isolated
## 1683 6 24 1009 2|14 low Small-Large isolated
## 1684 6 24 1009 2|14 low Small-Large isolated
## 1685 6 24 1009 2|14 low Small-Large isolated
## 1686 6 24 1009 2|14 low Small-Large isolated
## 1687 6 24 1009 2|14 low Small-Large isolated
## 1688 6 24 1009 2|14 low Small-Large isolated
## 1689 6 24 1009 2|14 low Small-Large isolated
## 1690 6 24 1009 2|14 low Small-Large isolated
## 1691 6 24 1009 2|14 low Small-Large isolated
## 1692 6 24 1009 2|14 low Small-Large isolated
## 1693 6 24 1009 2|14 low Small-Large isolated
## 1694 6 24 1009 2|14 low Small-Large isolated
## 1695 6 24 1009 2|14 low Small-Large isolated
## 1696 6 24 1009 2|14 low Small-Large isolated
## 1697 6 24 1009 2|14 low Small-Large isolated
## 1698 6 24 1009 2|14 low Small-Large isolated
## 1699 6 24 1009 2|14 low Small-Large isolated
## 1700 6 24 1009 2|14 low Small-Large isolated
## 1701 6 24 1009 2|14 low Small-Large isolated
## 1702 6 24 1009 2|14 low Small-Large isolated
## 1703 6 24 1009 2|14 low Small-Large isolated
## 1704 6 24 1009 2|14 low Small-Large isolated
## 1705 7 28 1009 2|14 low Small-Large isolated
## 1706 7 28 1009 2|14 low Small-Large isolated
## 1707 7 28 1009 2|14 low Small-Large isolated
## 1708 7 28 1009 2|14 low Small-Large isolated
## 1709 7 28 1009 2|14 low Small-Large isolated
## 1710 7 28 1009 2|14 low Small-Large isolated
## 1711 7 28 1009 2|14 low Small-Large isolated
## 1712 7 28 1009 2|14 low Small-Large isolated
## 1713 7 28 1009 2|14 low Small-Large isolated
## 1714 7 28 1009 2|14 low Small-Large isolated
## 1715 7 28 1009 2|14 low Small-Large isolated
## 1716 7 28 1009 2|14 low Small-Large isolated
## 1717 7 28 1009 2|14 low Small-Large isolated
## 1718 7 28 1009 2|14 low Small-Large isolated
## 1719 7 28 1009 2|14 low Small-Large isolated
## 1720 7 28 1009 2|14 low Small-Large isolated
## 1721 7 28 1009 2|14 low Small-Large isolated
## 1722 7 28 1009 2|14 low Small-Large isolated
## 1723 7 28 1009 2|14 low Small-Large isolated
## 1724 7 28 1009 2|14 low Small-Large isolated
## 1725 7 28 1009 2|14 low Small-Large isolated
## 1726 7 28 1009 2|14 low Small-Large isolated
## 1727 7 28 1009 2|14 low Small-Large isolated
## 1728 7 28 1009 2|14 low Small-Large isolated
## 1729 0 0 1010 2|15 low Small-Large isolated
## 1730 0 0 1010 2|15 low Small-Large isolated
## 1731 0 0 1010 2|15 low Small-Large isolated
## 1732 0 0 1010 2|15 low Small-Large isolated
## 1733 0 0 1010 2|15 low Small-Large isolated
## 1734 0 0 1010 2|15 low Small-Large isolated
## 1735 0 0 1010 2|15 low Small-Large isolated
## 1736 0 0 1010 2|15 low Small-Large isolated
## 1737 0 0 1010 2|15 low Small-Large isolated
## 1738 0 0 1010 2|15 low Small-Large isolated
## 1739 0 0 1010 2|15 low Small-Large isolated
## 1740 0 0 1010 2|15 low Small-Large isolated
## 1741 0 0 1010 2|15 low Small-Large isolated
## 1742 0 0 1010 2|15 low Small-Large isolated
## 1743 0 0 1010 2|15 low Small-Large isolated
## 1744 0 0 1010 2|15 low Small-Large isolated
## 1745 0 0 1010 2|15 low Small-Large isolated
## 1746 0 0 1010 2|15 low Small-Large isolated
## 1747 0 0 1010 2|15 low Small-Large isolated
## 1748 0 0 1010 2|15 low Small-Large isolated
## 1749 0 0 1010 2|15 low Small-Large isolated
## 1750 0 0 1010 2|15 low Small-Large isolated
## 1751 0 0 1010 2|15 low Small-Large isolated
## 1752 0 0 1010 2|15 low Small-Large isolated
## 1753 1 4 1010 2|15 low Small-Large isolated
## 1754 1 4 1010 2|15 low Small-Large isolated
## 1755 1 4 1010 2|15 low Small-Large isolated
## 1756 1 4 1010 2|15 low Small-Large isolated
## 1757 1 4 1010 2|15 low Small-Large isolated
## 1758 1 4 1010 2|15 low Small-Large isolated
## 1759 1 4 1010 2|15 low Small-Large isolated
## 1760 1 4 1010 2|15 low Small-Large isolated
## 1761 1 4 1010 2|15 low Small-Large isolated
## 1762 1 4 1010 2|15 low Small-Large isolated
## 1763 1 4 1010 2|15 low Small-Large isolated
## 1764 1 4 1010 2|15 low Small-Large isolated
## 1765 1 4 1010 2|15 low Small-Large isolated
## 1766 1 4 1010 2|15 low Small-Large isolated
## 1767 1 4 1010 2|15 low Small-Large isolated
## 1768 1 4 1010 2|15 low Small-Large isolated
## 1769 1 4 1010 2|15 low Small-Large isolated
## 1770 1 4 1010 2|15 low Small-Large isolated
## 1771 1 4 1010 2|15 low Small-Large isolated
## 1772 1 4 1010 2|15 low Small-Large isolated
## 1773 1 4 1010 2|15 low Small-Large isolated
## 1774 1 4 1010 2|15 low Small-Large isolated
## 1775 1 4 1010 2|15 low Small-Large isolated
## 1776 1 4 1010 2|15 low Small-Large isolated
## 1777 2 8 1010 2|15 low Small-Large isolated
## 1778 2 8 1010 2|15 low Small-Large isolated
## 1779 2 8 1010 2|15 low Small-Large isolated
## 1780 2 8 1010 2|15 low Small-Large isolated
## 1781 2 8 1010 2|15 low Small-Large isolated
## 1782 2 8 1010 2|15 low Small-Large isolated
## 1783 2 8 1010 2|15 low Small-Large isolated
## 1784 2 8 1010 2|15 low Small-Large isolated
## 1785 2 8 1010 2|15 low Small-Large isolated
## 1786 2 8 1010 2|15 low Small-Large isolated
## 1787 2 8 1010 2|15 low Small-Large isolated
## 1788 2 8 1010 2|15 low Small-Large isolated
## 1789 2 8 1010 2|15 low Small-Large isolated
## 1790 2 8 1010 2|15 low Small-Large isolated
## 1791 2 8 1010 2|15 low Small-Large isolated
## 1792 2 8 1010 2|15 low Small-Large isolated
## 1793 2 8 1010 2|15 low Small-Large isolated
## 1794 2 8 1010 2|15 low Small-Large isolated
## 1795 2 8 1010 2|15 low Small-Large isolated
## 1796 2 8 1010 2|15 low Small-Large isolated
## 1797 2 8 1010 2|15 low Small-Large isolated
## 1798 2 8 1010 2|15 low Small-Large isolated
## 1799 2 8 1010 2|15 low Small-Large isolated
## 1800 2 8 1010 2|15 low Small-Large isolated
## 1801 3 12 1010 2|15 low Small-Large isolated
## 1802 3 12 1010 2|15 low Small-Large isolated
## 1803 3 12 1010 2|15 low Small-Large isolated
## 1804 3 12 1010 2|15 low Small-Large isolated
## 1805 3 12 1010 2|15 low Small-Large isolated
## 1806 3 12 1010 2|15 low Small-Large isolated
## 1807 3 12 1010 2|15 low Small-Large isolated
## 1808 3 12 1010 2|15 low Small-Large isolated
## 1809 3 12 1010 2|15 low Small-Large isolated
## 1810 3 12 1010 2|15 low Small-Large isolated
## 1811 3 12 1010 2|15 low Small-Large isolated
## 1812 3 12 1010 2|15 low Small-Large isolated
## 1813 3 12 1010 2|15 low Small-Large isolated
## 1814 3 12 1010 2|15 low Small-Large isolated
## 1815 3 12 1010 2|15 low Small-Large isolated
## 1816 3 12 1010 2|15 low Small-Large isolated
## 1817 3 12 1010 2|15 low Small-Large isolated
## 1818 3 12 1010 2|15 low Small-Large isolated
## 1819 3 12 1010 2|15 low Small-Large isolated
## 1820 3 12 1010 2|15 low Small-Large isolated
## 1821 3 12 1010 2|15 low Small-Large isolated
## 1822 3 12 1010 2|15 low Small-Large isolated
## 1823 3 12 1010 2|15 low Small-Large isolated
## 1824 3 12 1010 2|15 low Small-Large isolated
## 1825 4 16 1010 2|15 low Small-Large isolated
## 1826 4 16 1010 2|15 low Small-Large isolated
## 1827 4 16 1010 2|15 low Small-Large isolated
## 1828 4 16 1010 2|15 low Small-Large isolated
## 1829 4 16 1010 2|15 low Small-Large isolated
## 1830 4 16 1010 2|15 low Small-Large isolated
## 1831 4 16 1010 2|15 low Small-Large isolated
## 1832 4 16 1010 2|15 low Small-Large isolated
## 1833 4 16 1010 2|15 low Small-Large isolated
## 1834 4 16 1010 2|15 low Small-Large isolated
## 1835 4 16 1010 2|15 low Small-Large isolated
## 1836 4 16 1010 2|15 low Small-Large isolated
## 1837 4 16 1010 2|15 low Small-Large isolated
## 1838 4 16 1010 2|15 low Small-Large isolated
## 1839 4 16 1010 2|15 low Small-Large isolated
## 1840 4 16 1010 2|15 low Small-Large isolated
## 1841 4 16 1010 2|15 low Small-Large isolated
## 1842 4 16 1010 2|15 low Small-Large isolated
## 1843 4 16 1010 2|15 low Small-Large isolated
## 1844 4 16 1010 2|15 low Small-Large isolated
## 1845 4 16 1010 2|15 low Small-Large isolated
## 1846 4 16 1010 2|15 low Small-Large isolated
## 1847 4 16 1010 2|15 low Small-Large isolated
## 1848 4 16 1010 2|15 low Small-Large isolated
## 1849 5 20 1010 2|15 low Small-Large isolated
## 1850 5 20 1010 2|15 low Small-Large isolated
## 1851 5 20 1010 2|15 low Small-Large isolated
## 1852 5 20 1010 2|15 low Small-Large isolated
## 1853 5 20 1010 2|15 low Small-Large isolated
## 1854 5 20 1010 2|15 low Small-Large isolated
## 1855 5 20 1010 2|15 low Small-Large isolated
## 1856 5 20 1010 2|15 low Small-Large isolated
## 1857 5 20 1010 2|15 low Small-Large isolated
## 1858 5 20 1010 2|15 low Small-Large isolated
## 1859 5 20 1010 2|15 low Small-Large isolated
## 1860 5 20 1010 2|15 low Small-Large isolated
## 1861 5 20 1010 2|15 low Small-Large isolated
## 1862 5 20 1010 2|15 low Small-Large isolated
## 1863 5 20 1010 2|15 low Small-Large isolated
## 1864 5 20 1010 2|15 low Small-Large isolated
## 1865 5 20 1010 2|15 low Small-Large isolated
## 1866 5 20 1010 2|15 low Small-Large isolated
## 1867 5 20 1010 2|15 low Small-Large isolated
## 1868 5 20 1010 2|15 low Small-Large isolated
## 1869 5 20 1010 2|15 low Small-Large isolated
## 1870 5 20 1010 2|15 low Small-Large isolated
## 1871 5 20 1010 2|15 low Small-Large isolated
## 1872 5 20 1010 2|15 low Small-Large isolated
## 1873 6 24 1010 2|15 low Small-Large isolated
## 1874 6 24 1010 2|15 low Small-Large isolated
## 1875 6 24 1010 2|15 low Small-Large isolated
## 1876 6 24 1010 2|15 low Small-Large isolated
## 1877 6 24 1010 2|15 low Small-Large isolated
## 1878 6 24 1010 2|15 low Small-Large isolated
## 1879 6 24 1010 2|15 low Small-Large isolated
## 1880 6 24 1010 2|15 low Small-Large isolated
## 1881 6 24 1010 2|15 low Small-Large isolated
## 1882 6 24 1010 2|15 low Small-Large isolated
## 1883 6 24 1010 2|15 low Small-Large isolated
## 1884 6 24 1010 2|15 low Small-Large isolated
## 1885 6 24 1010 2|15 low Small-Large isolated
## 1886 6 24 1010 2|15 low Small-Large isolated
## 1887 6 24 1010 2|15 low Small-Large isolated
## 1888 6 24 1010 2|15 low Small-Large isolated
## 1889 6 24 1010 2|15 low Small-Large isolated
## 1890 6 24 1010 2|15 low Small-Large isolated
## 1891 6 24 1010 2|15 low Small-Large isolated
## 1892 6 24 1010 2|15 low Small-Large isolated
## 1893 6 24 1010 2|15 low Small-Large isolated
## 1894 6 24 1010 2|15 low Small-Large isolated
## 1895 6 24 1010 2|15 low Small-Large isolated
## 1896 6 24 1010 2|15 low Small-Large isolated
## 1897 7 28 1010 2|15 low Small-Large isolated
## 1898 7 28 1010 2|15 low Small-Large isolated
## 1899 7 28 1010 2|15 low Small-Large isolated
## 1900 7 28 1010 2|15 low Small-Large isolated
## 1901 7 28 1010 2|15 low Small-Large isolated
## 1902 7 28 1010 2|15 low Small-Large isolated
## 1903 7 28 1010 2|15 low Small-Large isolated
## 1904 7 28 1010 2|15 low Small-Large isolated
## 1905 7 28 1010 2|15 low Small-Large isolated
## 1906 7 28 1010 2|15 low Small-Large isolated
## 1907 7 28 1010 2|15 low Small-Large isolated
## 1908 7 28 1010 2|15 low Small-Large isolated
## 1909 7 28 1010 2|15 low Small-Large isolated
## 1910 7 28 1010 2|15 low Small-Large isolated
## 1911 7 28 1010 2|15 low Small-Large isolated
## 1912 7 28 1010 2|15 low Small-Large isolated
## 1913 7 28 1010 2|15 low Small-Large isolated
## 1914 7 28 1010 2|15 low Small-Large isolated
## 1915 7 28 1010 2|15 low Small-Large isolated
## 1916 7 28 1010 2|15 low Small-Large isolated
## 1917 7 28 1010 2|15 low Small-Large isolated
## 1918 7 28 1010 2|15 low Small-Large isolated
## 1919 7 28 1010 2|15 low Small-Large isolated
## 1920 7 28 1010 2|15 low Small-Large isolated
## 1921 0 0 1011 3|11 low Small-Large isolated
## 1922 0 0 1011 3|11 low Small-Large isolated
## 1923 0 0 1011 3|11 low Small-Large isolated
## 1924 0 0 1011 3|11 low Small-Large isolated
## 1925 0 0 1011 3|11 low Small-Large isolated
## 1926 0 0 1011 3|11 low Small-Large isolated
## 1927 0 0 1011 3|11 low Small-Large isolated
## 1928 0 0 1011 3|11 low Small-Large isolated
## 1929 0 0 1011 3|11 low Small-Large isolated
## 1930 0 0 1011 3|11 low Small-Large isolated
## 1931 0 0 1011 3|11 low Small-Large isolated
## 1932 0 0 1011 3|11 low Small-Large isolated
## 1933 0 0 1011 3|11 low Small-Large isolated
## 1934 0 0 1011 3|11 low Small-Large isolated
## 1935 0 0 1011 3|11 low Small-Large isolated
## 1936 0 0 1011 3|11 low Small-Large isolated
## 1937 0 0 1011 3|11 low Small-Large isolated
## 1938 0 0 1011 3|11 low Small-Large isolated
## 1939 0 0 1011 3|11 low Small-Large isolated
## 1940 0 0 1011 3|11 low Small-Large isolated
## 1941 0 0 1011 3|11 low Small-Large isolated
## 1942 0 0 1011 3|11 low Small-Large isolated
## 1943 0 0 1011 3|11 low Small-Large isolated
## 1944 0 0 1011 3|11 low Small-Large isolated
## 1945 1 4 1011 3|11 low Small-Large isolated
## 1946 1 4 1011 3|11 low Small-Large isolated
## 1947 1 4 1011 3|11 low Small-Large isolated
## 1948 1 4 1011 3|11 low Small-Large isolated
## 1949 1 4 1011 3|11 low Small-Large isolated
## 1950 1 4 1011 3|11 low Small-Large isolated
## 1951 1 4 1011 3|11 low Small-Large isolated
## 1952 1 4 1011 3|11 low Small-Large isolated
## 1953 1 4 1011 3|11 low Small-Large isolated
## 1954 1 4 1011 3|11 low Small-Large isolated
## 1955 1 4 1011 3|11 low Small-Large isolated
## 1956 1 4 1011 3|11 low Small-Large isolated
## 1957 1 4 1011 3|11 low Small-Large isolated
## 1958 1 4 1011 3|11 low Small-Large isolated
## 1959 1 4 1011 3|11 low Small-Large isolated
## 1960 1 4 1011 3|11 low Small-Large isolated
## 1961 1 4 1011 3|11 low Small-Large isolated
## 1962 1 4 1011 3|11 low Small-Large isolated
## 1963 1 4 1011 3|11 low Small-Large isolated
## 1964 1 4 1011 3|11 low Small-Large isolated
## 1965 1 4 1011 3|11 low Small-Large isolated
## 1966 1 4 1011 3|11 low Small-Large isolated
## 1967 1 4 1011 3|11 low Small-Large isolated
## 1968 1 4 1011 3|11 low Small-Large isolated
## 1969 2 8 1011 3|11 low Small-Large isolated
## 1970 2 8 1011 3|11 low Small-Large isolated
## 1971 2 8 1011 3|11 low Small-Large isolated
## 1972 2 8 1011 3|11 low Small-Large isolated
## 1973 2 8 1011 3|11 low Small-Large isolated
## 1974 2 8 1011 3|11 low Small-Large isolated
## 1975 2 8 1011 3|11 low Small-Large isolated
## 1976 2 8 1011 3|11 low Small-Large isolated
## 1977 2 8 1011 3|11 low Small-Large isolated
## 1978 2 8 1011 3|11 low Small-Large isolated
## 1979 2 8 1011 3|11 low Small-Large isolated
## 1980 2 8 1011 3|11 low Small-Large isolated
## 1981 2 8 1011 3|11 low Small-Large isolated
## 1982 2 8 1011 3|11 low Small-Large isolated
## 1983 2 8 1011 3|11 low Small-Large isolated
## 1984 2 8 1011 3|11 low Small-Large isolated
## 1985 2 8 1011 3|11 low Small-Large isolated
## 1986 2 8 1011 3|11 low Small-Large isolated
## 1987 2 8 1011 3|11 low Small-Large isolated
## 1988 2 8 1011 3|11 low Small-Large isolated
## 1989 2 8 1011 3|11 low Small-Large isolated
## 1990 2 8 1011 3|11 low Small-Large isolated
## 1991 2 8 1011 3|11 low Small-Large isolated
## 1992 2 8 1011 3|11 low Small-Large isolated
## 1993 3 12 1011 3|11 low Small-Large isolated
## 1994 3 12 1011 3|11 low Small-Large isolated
## 1995 3 12 1011 3|11 low Small-Large isolated
## 1996 3 12 1011 3|11 low Small-Large isolated
## 1997 3 12 1011 3|11 low Small-Large isolated
## 1998 3 12 1011 3|11 low Small-Large isolated
## 1999 3 12 1011 3|11 low Small-Large isolated
## 2000 3 12 1011 3|11 low Small-Large isolated
## 2001 3 12 1011 3|11 low Small-Large isolated
## 2002 3 12 1011 3|11 low Small-Large isolated
## 2003 3 12 1011 3|11 low Small-Large isolated
## 2004 3 12 1011 3|11 low Small-Large isolated
## 2005 3 12 1011 3|11 low Small-Large isolated
## 2006 3 12 1011 3|11 low Small-Large isolated
## 2007 3 12 1011 3|11 low Small-Large isolated
## 2008 3 12 1011 3|11 low Small-Large isolated
## 2009 3 12 1011 3|11 low Small-Large isolated
## 2010 3 12 1011 3|11 low Small-Large isolated
## 2011 3 12 1011 3|11 low Small-Large isolated
## 2012 3 12 1011 3|11 low Small-Large isolated
## 2013 3 12 1011 3|11 low Small-Large isolated
## 2014 3 12 1011 3|11 low Small-Large isolated
## 2015 3 12 1011 3|11 low Small-Large isolated
## 2016 3 12 1011 3|11 low Small-Large isolated
## 2017 4 16 1011 3|11 low Small-Large isolated
## 2018 4 16 1011 3|11 low Small-Large isolated
## 2019 4 16 1011 3|11 low Small-Large isolated
## 2020 4 16 1011 3|11 low Small-Large isolated
## 2021 4 16 1011 3|11 low Small-Large isolated
## 2022 4 16 1011 3|11 low Small-Large isolated
## 2023 4 16 1011 3|11 low Small-Large isolated
## 2024 4 16 1011 3|11 low Small-Large isolated
## 2025 4 16 1011 3|11 low Small-Large isolated
## 2026 4 16 1011 3|11 low Small-Large isolated
## 2027 4 16 1011 3|11 low Small-Large isolated
## 2028 4 16 1011 3|11 low Small-Large isolated
## 2029 4 16 1011 3|11 low Small-Large isolated
## 2030 4 16 1011 3|11 low Small-Large isolated
## 2031 4 16 1011 3|11 low Small-Large isolated
## 2032 4 16 1011 3|11 low Small-Large isolated
## 2033 4 16 1011 3|11 low Small-Large isolated
## 2034 4 16 1011 3|11 low Small-Large isolated
## 2035 4 16 1011 3|11 low Small-Large isolated
## 2036 4 16 1011 3|11 low Small-Large isolated
## 2037 4 16 1011 3|11 low Small-Large isolated
## 2038 4 16 1011 3|11 low Small-Large isolated
## 2039 4 16 1011 3|11 low Small-Large isolated
## 2040 4 16 1011 3|11 low Small-Large isolated
## 2041 5 20 1011 3|11 low Small-Large isolated
## 2042 5 20 1011 3|11 low Small-Large isolated
## 2043 5 20 1011 3|11 low Small-Large isolated
## 2044 5 20 1011 3|11 low Small-Large isolated
## 2045 5 20 1011 3|11 low Small-Large isolated
## 2046 5 20 1011 3|11 low Small-Large isolated
## 2047 5 20 1011 3|11 low Small-Large isolated
## 2048 5 20 1011 3|11 low Small-Large isolated
## 2049 5 20 1011 3|11 low Small-Large isolated
## 2050 5 20 1011 3|11 low Small-Large isolated
## 2051 5 20 1011 3|11 low Small-Large isolated
## 2052 5 20 1011 3|11 low Small-Large isolated
## 2053 5 20 1011 3|11 low Small-Large isolated
## 2054 5 20 1011 3|11 low Small-Large isolated
## 2055 5 20 1011 3|11 low Small-Large isolated
## 2056 5 20 1011 3|11 low Small-Large isolated
## 2057 5 20 1011 3|11 low Small-Large isolated
## 2058 5 20 1011 3|11 low Small-Large isolated
## 2059 5 20 1011 3|11 low Small-Large isolated
## 2060 5 20 1011 3|11 low Small-Large isolated
## 2061 5 20 1011 3|11 low Small-Large isolated
## 2062 5 20 1011 3|11 low Small-Large isolated
## 2063 5 20 1011 3|11 low Small-Large isolated
## 2064 5 20 1011 3|11 low Small-Large isolated
## 2065 6 24 1011 3|11 low Small-Large isolated
## 2066 6 24 1011 3|11 low Small-Large isolated
## 2067 6 24 1011 3|11 low Small-Large isolated
## 2068 6 24 1011 3|11 low Small-Large isolated
## 2069 6 24 1011 3|11 low Small-Large isolated
## 2070 6 24 1011 3|11 low Small-Large isolated
## 2071 6 24 1011 3|11 low Small-Large isolated
## 2072 6 24 1011 3|11 low Small-Large isolated
## 2073 6 24 1011 3|11 low Small-Large isolated
## 2074 6 24 1011 3|11 low Small-Large isolated
## 2075 6 24 1011 3|11 low Small-Large isolated
## 2076 6 24 1011 3|11 low Small-Large isolated
## 2077 6 24 1011 3|11 low Small-Large isolated
## 2078 6 24 1011 3|11 low Small-Large isolated
## 2079 6 24 1011 3|11 low Small-Large isolated
## 2080 6 24 1011 3|11 low Small-Large isolated
## 2081 6 24 1011 3|11 low Small-Large isolated
## 2082 6 24 1011 3|11 low Small-Large isolated
## 2083 6 24 1011 3|11 low Small-Large isolated
## 2084 6 24 1011 3|11 low Small-Large isolated
## 2085 6 24 1011 3|11 low Small-Large isolated
## 2086 6 24 1011 3|11 low Small-Large isolated
## 2087 6 24 1011 3|11 low Small-Large isolated
## 2088 6 24 1011 3|11 low Small-Large isolated
## 2089 7 28 1011 3|11 low Small-Large isolated
## 2090 7 28 1011 3|11 low Small-Large isolated
## 2091 7 28 1011 3|11 low Small-Large isolated
## 2092 7 28 1011 3|11 low Small-Large isolated
## 2093 7 28 1011 3|11 low Small-Large isolated
## 2094 7 28 1011 3|11 low Small-Large isolated
## 2095 7 28 1011 3|11 low Small-Large isolated
## 2096 7 28 1011 3|11 low Small-Large isolated
## 2097 7 28 1011 3|11 low Small-Large isolated
## 2098 7 28 1011 3|11 low Small-Large isolated
## 2099 7 28 1011 3|11 low Small-Large isolated
## 2100 7 28 1011 3|11 low Small-Large isolated
## 2101 7 28 1011 3|11 low Small-Large isolated
## 2102 7 28 1011 3|11 low Small-Large isolated
## 2103 7 28 1011 3|11 low Small-Large isolated
## 2104 7 28 1011 3|11 low Small-Large isolated
## 2105 7 28 1011 3|11 low Small-Large isolated
## 2106 7 28 1011 3|11 low Small-Large isolated
## 2107 7 28 1011 3|11 low Small-Large isolated
## 2108 7 28 1011 3|11 low Small-Large isolated
## 2109 7 28 1011 3|11 low Small-Large isolated
## 2110 7 28 1011 3|11 low Small-Large isolated
## 2111 7 28 1011 3|11 low Small-Large isolated
## 2112 7 28 1011 3|11 low Small-Large isolated
## 2113 0 0 1012 3|12 low Small-Large isolated
## 2114 0 0 1012 3|12 low Small-Large isolated
## 2115 0 0 1012 3|12 low Small-Large isolated
## 2116 0 0 1012 3|12 low Small-Large isolated
## 2117 0 0 1012 3|12 low Small-Large isolated
## 2118 0 0 1012 3|12 low Small-Large isolated
## 2119 0 0 1012 3|12 low Small-Large isolated
## 2120 0 0 1012 3|12 low Small-Large isolated
## 2121 0 0 1012 3|12 low Small-Large isolated
## 2122 0 0 1012 3|12 low Small-Large isolated
## 2123 0 0 1012 3|12 low Small-Large isolated
## 2124 0 0 1012 3|12 low Small-Large isolated
## 2125 0 0 1012 3|12 low Small-Large isolated
## 2126 0 0 1012 3|12 low Small-Large isolated
## 2127 0 0 1012 3|12 low Small-Large isolated
## 2128 0 0 1012 3|12 low Small-Large isolated
## 2129 0 0 1012 3|12 low Small-Large isolated
## 2130 0 0 1012 3|12 low Small-Large isolated
## 2131 0 0 1012 3|12 low Small-Large isolated
## 2132 0 0 1012 3|12 low Small-Large isolated
## 2133 0 0 1012 3|12 low Small-Large isolated
## 2134 0 0 1012 3|12 low Small-Large isolated
## 2135 0 0 1012 3|12 low Small-Large isolated
## 2136 0 0 1012 3|12 low Small-Large isolated
## 2137 1 4 1012 3|12 low Small-Large isolated
## 2138 1 4 1012 3|12 low Small-Large isolated
## 2139 1 4 1012 3|12 low Small-Large isolated
## 2140 1 4 1012 3|12 low Small-Large isolated
## 2141 1 4 1012 3|12 low Small-Large isolated
## 2142 1 4 1012 3|12 low Small-Large isolated
## 2143 1 4 1012 3|12 low Small-Large isolated
## 2144 1 4 1012 3|12 low Small-Large isolated
## 2145 1 4 1012 3|12 low Small-Large isolated
## 2146 1 4 1012 3|12 low Small-Large isolated
## 2147 1 4 1012 3|12 low Small-Large isolated
## 2148 1 4 1012 3|12 low Small-Large isolated
## 2149 1 4 1012 3|12 low Small-Large isolated
## 2150 1 4 1012 3|12 low Small-Large isolated
## 2151 1 4 1012 3|12 low Small-Large isolated
## 2152 1 4 1012 3|12 low Small-Large isolated
## 2153 1 4 1012 3|12 low Small-Large isolated
## 2154 1 4 1012 3|12 low Small-Large isolated
## 2155 1 4 1012 3|12 low Small-Large isolated
## 2156 1 4 1012 3|12 low Small-Large isolated
## 2157 1 4 1012 3|12 low Small-Large isolated
## 2158 1 4 1012 3|12 low Small-Large isolated
## 2159 1 4 1012 3|12 low Small-Large isolated
## 2160 1 4 1012 3|12 low Small-Large isolated
## 2161 2 8 1012 3|12 low Small-Large isolated
## 2162 2 8 1012 3|12 low Small-Large isolated
## 2163 2 8 1012 3|12 low Small-Large isolated
## 2164 2 8 1012 3|12 low Small-Large isolated
## 2165 2 8 1012 3|12 low Small-Large isolated
## 2166 2 8 1012 3|12 low Small-Large isolated
## 2167 2 8 1012 3|12 low Small-Large isolated
## 2168 2 8 1012 3|12 low Small-Large isolated
## 2169 2 8 1012 3|12 low Small-Large isolated
## 2170 2 8 1012 3|12 low Small-Large isolated
## 2171 2 8 1012 3|12 low Small-Large isolated
## 2172 2 8 1012 3|12 low Small-Large isolated
## 2173 2 8 1012 3|12 low Small-Large isolated
## 2174 2 8 1012 3|12 low Small-Large isolated
## 2175 2 8 1012 3|12 low Small-Large isolated
## 2176 2 8 1012 3|12 low Small-Large isolated
## 2177 2 8 1012 3|12 low Small-Large isolated
## 2178 2 8 1012 3|12 low Small-Large isolated
## 2179 2 8 1012 3|12 low Small-Large isolated
## 2180 2 8 1012 3|12 low Small-Large isolated
## 2181 2 8 1012 3|12 low Small-Large isolated
## 2182 2 8 1012 3|12 low Small-Large isolated
## 2183 2 8 1012 3|12 low Small-Large isolated
## 2184 2 8 1012 3|12 low Small-Large isolated
## 2185 3 12 1012 3|12 low Small-Large isolated
## 2186 3 12 1012 3|12 low Small-Large isolated
## 2187 3 12 1012 3|12 low Small-Large isolated
## 2188 3 12 1012 3|12 low Small-Large isolated
## 2189 3 12 1012 3|12 low Small-Large isolated
## 2190 3 12 1012 3|12 low Small-Large isolated
## 2191 3 12 1012 3|12 low Small-Large isolated
## 2192 3 12 1012 3|12 low Small-Large isolated
## 2193 3 12 1012 3|12 low Small-Large isolated
## 2194 3 12 1012 3|12 low Small-Large isolated
## 2195 3 12 1012 3|12 low Small-Large isolated
## 2196 3 12 1012 3|12 low Small-Large isolated
## 2197 3 12 1012 3|12 low Small-Large isolated
## 2198 3 12 1012 3|12 low Small-Large isolated
## 2199 3 12 1012 3|12 low Small-Large isolated
## 2200 3 12 1012 3|12 low Small-Large isolated
## 2201 3 12 1012 3|12 low Small-Large isolated
## 2202 3 12 1012 3|12 low Small-Large isolated
## 2203 3 12 1012 3|12 low Small-Large isolated
## 2204 3 12 1012 3|12 low Small-Large isolated
## 2205 3 12 1012 3|12 low Small-Large isolated
## 2206 3 12 1012 3|12 low Small-Large isolated
## 2207 3 12 1012 3|12 low Small-Large isolated
## 2208 3 12 1012 3|12 low Small-Large isolated
## 2209 4 16 1012 3|12 low Small-Large isolated
## 2210 4 16 1012 3|12 low Small-Large isolated
## 2211 4 16 1012 3|12 low Small-Large isolated
## 2212 4 16 1012 3|12 low Small-Large isolated
## 2213 4 16 1012 3|12 low Small-Large isolated
## 2214 4 16 1012 3|12 low Small-Large isolated
## 2215 4 16 1012 3|12 low Small-Large isolated
## 2216 4 16 1012 3|12 low Small-Large isolated
## 2217 4 16 1012 3|12 low Small-Large isolated
## 2218 4 16 1012 3|12 low Small-Large isolated
## 2219 4 16 1012 3|12 low Small-Large isolated
## 2220 4 16 1012 3|12 low Small-Large isolated
## 2221 4 16 1012 3|12 low Small-Large isolated
## 2222 4 16 1012 3|12 low Small-Large isolated
## 2223 4 16 1012 3|12 low Small-Large isolated
## 2224 4 16 1012 3|12 low Small-Large isolated
## 2225 4 16 1012 3|12 low Small-Large isolated
## 2226 4 16 1012 3|12 low Small-Large isolated
## 2227 4 16 1012 3|12 low Small-Large isolated
## 2228 4 16 1012 3|12 low Small-Large isolated
## 2229 4 16 1012 3|12 low Small-Large isolated
## 2230 4 16 1012 3|12 low Small-Large isolated
## 2231 4 16 1012 3|12 low Small-Large isolated
## 2232 4 16 1012 3|12 low Small-Large isolated
## 2233 5 20 1012 3|12 low Small-Large isolated
## 2234 5 20 1012 3|12 low Small-Large isolated
## 2235 5 20 1012 3|12 low Small-Large isolated
## 2236 5 20 1012 3|12 low Small-Large isolated
## 2237 5 20 1012 3|12 low Small-Large isolated
## 2238 5 20 1012 3|12 low Small-Large isolated
## 2239 5 20 1012 3|12 low Small-Large isolated
## 2240 5 20 1012 3|12 low Small-Large isolated
## 2241 5 20 1012 3|12 low Small-Large isolated
## 2242 5 20 1012 3|12 low Small-Large isolated
## 2243 5 20 1012 3|12 low Small-Large isolated
## 2244 5 20 1012 3|12 low Small-Large isolated
## 2245 5 20 1012 3|12 low Small-Large isolated
## 2246 5 20 1012 3|12 low Small-Large isolated
## 2247 5 20 1012 3|12 low Small-Large isolated
## 2248 5 20 1012 3|12 low Small-Large isolated
## 2249 5 20 1012 3|12 low Small-Large isolated
## 2250 5 20 1012 3|12 low Small-Large isolated
## 2251 5 20 1012 3|12 low Small-Large isolated
## 2252 5 20 1012 3|12 low Small-Large isolated
## 2253 5 20 1012 3|12 low Small-Large isolated
## 2254 5 20 1012 3|12 low Small-Large isolated
## 2255 5 20 1012 3|12 low Small-Large isolated
## 2256 5 20 1012 3|12 low Small-Large isolated
## 2257 6 24 1012 3|12 low Small-Large isolated
## 2258 6 24 1012 3|12 low Small-Large isolated
## 2259 6 24 1012 3|12 low Small-Large isolated
## 2260 6 24 1012 3|12 low Small-Large isolated
## 2261 6 24 1012 3|12 low Small-Large isolated
## 2262 6 24 1012 3|12 low Small-Large isolated
## 2263 6 24 1012 3|12 low Small-Large isolated
## 2264 6 24 1012 3|12 low Small-Large isolated
## 2265 6 24 1012 3|12 low Small-Large isolated
## 2266 6 24 1012 3|12 low Small-Large isolated
## 2267 6 24 1012 3|12 low Small-Large isolated
## 2268 6 24 1012 3|12 low Small-Large isolated
## 2269 6 24 1012 3|12 low Small-Large isolated
## 2270 6 24 1012 3|12 low Small-Large isolated
## 2271 6 24 1012 3|12 low Small-Large isolated
## 2272 6 24 1012 3|12 low Small-Large isolated
## 2273 6 24 1012 3|12 low Small-Large isolated
## 2274 6 24 1012 3|12 low Small-Large isolated
## 2275 6 24 1012 3|12 low Small-Large isolated
## 2276 6 24 1012 3|12 low Small-Large isolated
## 2277 6 24 1012 3|12 low Small-Large isolated
## 2278 6 24 1012 3|12 low Small-Large isolated
## 2279 6 24 1012 3|12 low Small-Large isolated
## 2280 6 24 1012 3|12 low Small-Large isolated
## 2281 7 28 1012 3|12 low Small-Large isolated
## 2282 7 28 1012 3|12 low Small-Large isolated
## 2283 7 28 1012 3|12 low Small-Large isolated
## 2284 7 28 1012 3|12 low Small-Large isolated
## 2285 7 28 1012 3|12 low Small-Large isolated
## 2286 7 28 1012 3|12 low Small-Large isolated
## 2287 7 28 1012 3|12 low Small-Large isolated
## 2288 7 28 1012 3|12 low Small-Large isolated
## 2289 7 28 1012 3|12 low Small-Large isolated
## 2290 7 28 1012 3|12 low Small-Large isolated
## 2291 7 28 1012 3|12 low Small-Large isolated
## 2292 7 28 1012 3|12 low Small-Large isolated
## 2293 7 28 1012 3|12 low Small-Large isolated
## 2294 7 28 1012 3|12 low Small-Large isolated
## 2295 7 28 1012 3|12 low Small-Large isolated
## 2296 7 28 1012 3|12 low Small-Large isolated
## 2297 7 28 1012 3|12 low Small-Large isolated
## 2298 7 28 1012 3|12 low Small-Large isolated
## 2299 7 28 1012 3|12 low Small-Large isolated
## 2300 7 28 1012 3|12 low Small-Large isolated
## 2301 7 28 1012 3|12 low Small-Large isolated
## 2302 7 28 1012 3|12 low Small-Large isolated
## 2303 7 28 1012 3|12 low Small-Large isolated
## 2304 7 28 1012 3|12 low Small-Large isolated
## 2305 0 0 1013 3|13 low Small-Large isolated
## 2306 0 0 1013 3|13 low Small-Large isolated
## 2307 0 0 1013 3|13 low Small-Large isolated
## 2308 0 0 1013 3|13 low Small-Large isolated
## 2309 0 0 1013 3|13 low Small-Large isolated
## 2310 0 0 1013 3|13 low Small-Large isolated
## 2311 0 0 1013 3|13 low Small-Large isolated
## 2312 0 0 1013 3|13 low Small-Large isolated
## 2313 0 0 1013 3|13 low Small-Large isolated
## 2314 0 0 1013 3|13 low Small-Large isolated
## 2315 0 0 1013 3|13 low Small-Large isolated
## 2316 0 0 1013 3|13 low Small-Large isolated
## 2317 0 0 1013 3|13 low Small-Large isolated
## 2318 0 0 1013 3|13 low Small-Large isolated
## 2319 0 0 1013 3|13 low Small-Large isolated
## 2320 0 0 1013 3|13 low Small-Large isolated
## 2321 0 0 1013 3|13 low Small-Large isolated
## 2322 0 0 1013 3|13 low Small-Large isolated
## 2323 0 0 1013 3|13 low Small-Large isolated
## 2324 0 0 1013 3|13 low Small-Large isolated
## 2325 0 0 1013 3|13 low Small-Large isolated
## 2326 0 0 1013 3|13 low Small-Large isolated
## 2327 0 0 1013 3|13 low Small-Large isolated
## 2328 0 0 1013 3|13 low Small-Large isolated
## 2329 1 4 1013 3|13 low Small-Large isolated
## 2330 1 4 1013 3|13 low Small-Large isolated
## 2331 1 4 1013 3|13 low Small-Large isolated
## 2332 1 4 1013 3|13 low Small-Large isolated
## 2333 1 4 1013 3|13 low Small-Large isolated
## 2334 1 4 1013 3|13 low Small-Large isolated
## 2335 1 4 1013 3|13 low Small-Large isolated
## 2336 1 4 1013 3|13 low Small-Large isolated
## 2337 1 4 1013 3|13 low Small-Large isolated
## 2338 1 4 1013 3|13 low Small-Large isolated
## 2339 1 4 1013 3|13 low Small-Large isolated
## 2340 1 4 1013 3|13 low Small-Large isolated
## 2341 1 4 1013 3|13 low Small-Large isolated
## 2342 1 4 1013 3|13 low Small-Large isolated
## 2343 1 4 1013 3|13 low Small-Large isolated
## 2344 1 4 1013 3|13 low Small-Large isolated
## 2345 1 4 1013 3|13 low Small-Large isolated
## 2346 1 4 1013 3|13 low Small-Large isolated
## 2347 1 4 1013 3|13 low Small-Large isolated
## 2348 1 4 1013 3|13 low Small-Large isolated
## 2349 1 4 1013 3|13 low Small-Large isolated
## 2350 1 4 1013 3|13 low Small-Large isolated
## 2351 1 4 1013 3|13 low Small-Large isolated
## 2352 1 4 1013 3|13 low Small-Large isolated
## 2353 2 8 1013 3|13 low Small-Large isolated
## 2354 2 8 1013 3|13 low Small-Large isolated
## 2355 2 8 1013 3|13 low Small-Large isolated
## 2356 2 8 1013 3|13 low Small-Large isolated
## 2357 2 8 1013 3|13 low Small-Large isolated
## 2358 2 8 1013 3|13 low Small-Large isolated
## 2359 2 8 1013 3|13 low Small-Large isolated
## 2360 2 8 1013 3|13 low Small-Large isolated
## 2361 2 8 1013 3|13 low Small-Large isolated
## 2362 2 8 1013 3|13 low Small-Large isolated
## 2363 2 8 1013 3|13 low Small-Large isolated
## 2364 2 8 1013 3|13 low Small-Large isolated
## 2365 2 8 1013 3|13 low Small-Large isolated
## 2366 2 8 1013 3|13 low Small-Large isolated
## 2367 2 8 1013 3|13 low Small-Large isolated
## 2368 2 8 1013 3|13 low Small-Large isolated
## 2369 2 8 1013 3|13 low Small-Large isolated
## 2370 2 8 1013 3|13 low Small-Large isolated
## 2371 2 8 1013 3|13 low Small-Large isolated
## 2372 2 8 1013 3|13 low Small-Large isolated
## 2373 2 8 1013 3|13 low Small-Large isolated
## 2374 2 8 1013 3|13 low Small-Large isolated
## 2375 2 8 1013 3|13 low Small-Large isolated
## 2376 2 8 1013 3|13 low Small-Large isolated
## 2377 3 12 1013 3|13 low Small-Large isolated
## 2378 3 12 1013 3|13 low Small-Large isolated
## 2379 3 12 1013 3|13 low Small-Large isolated
## 2380 3 12 1013 3|13 low Small-Large isolated
## 2381 3 12 1013 3|13 low Small-Large isolated
## 2382 3 12 1013 3|13 low Small-Large isolated
## 2383 3 12 1013 3|13 low Small-Large isolated
## 2384 3 12 1013 3|13 low Small-Large isolated
## 2385 3 12 1013 3|13 low Small-Large isolated
## 2386 3 12 1013 3|13 low Small-Large isolated
## 2387 3 12 1013 3|13 low Small-Large isolated
## 2388 3 12 1013 3|13 low Small-Large isolated
## 2389 3 12 1013 3|13 low Small-Large isolated
## 2390 3 12 1013 3|13 low Small-Large isolated
## 2391 3 12 1013 3|13 low Small-Large isolated
## 2392 3 12 1013 3|13 low Small-Large isolated
## 2393 3 12 1013 3|13 low Small-Large isolated
## 2394 3 12 1013 3|13 low Small-Large isolated
## 2395 3 12 1013 3|13 low Small-Large isolated
## 2396 3 12 1013 3|13 low Small-Large isolated
## 2397 3 12 1013 3|13 low Small-Large isolated
## 2398 3 12 1013 3|13 low Small-Large isolated
## 2399 3 12 1013 3|13 low Small-Large isolated
## 2400 3 12 1013 3|13 low Small-Large isolated
## 2401 4 16 1013 3|13 low Small-Large isolated
## 2402 4 16 1013 3|13 low Small-Large isolated
## 2403 4 16 1013 3|13 low Small-Large isolated
## 2404 4 16 1013 3|13 low Small-Large isolated
## 2405 4 16 1013 3|13 low Small-Large isolated
## 2406 4 16 1013 3|13 low Small-Large isolated
## 2407 4 16 1013 3|13 low Small-Large isolated
## 2408 4 16 1013 3|13 low Small-Large isolated
## 2409 4 16 1013 3|13 low Small-Large isolated
## 2410 4 16 1013 3|13 low Small-Large isolated
## 2411 4 16 1013 3|13 low Small-Large isolated
## 2412 4 16 1013 3|13 low Small-Large isolated
## 2413 4 16 1013 3|13 low Small-Large isolated
## 2414 4 16 1013 3|13 low Small-Large isolated
## 2415 4 16 1013 3|13 low Small-Large isolated
## 2416 4 16 1013 3|13 low Small-Large isolated
## 2417 4 16 1013 3|13 low Small-Large isolated
## 2418 4 16 1013 3|13 low Small-Large isolated
## 2419 4 16 1013 3|13 low Small-Large isolated
## 2420 4 16 1013 3|13 low Small-Large isolated
## 2421 4 16 1013 3|13 low Small-Large isolated
## 2422 4 16 1013 3|13 low Small-Large isolated
## 2423 4 16 1013 3|13 low Small-Large isolated
## 2424 4 16 1013 3|13 low Small-Large isolated
## 2425 5 20 1013 3|13 low Small-Large isolated
## 2426 5 20 1013 3|13 low Small-Large isolated
## 2427 5 20 1013 3|13 low Small-Large isolated
## 2428 5 20 1013 3|13 low Small-Large isolated
## 2429 5 20 1013 3|13 low Small-Large isolated
## 2430 5 20 1013 3|13 low Small-Large isolated
## 2431 5 20 1013 3|13 low Small-Large isolated
## 2432 5 20 1013 3|13 low Small-Large isolated
## 2433 5 20 1013 3|13 low Small-Large isolated
## 2434 5 20 1013 3|13 low Small-Large isolated
## 2435 5 20 1013 3|13 low Small-Large isolated
## 2436 5 20 1013 3|13 low Small-Large isolated
## 2437 5 20 1013 3|13 low Small-Large isolated
## 2438 5 20 1013 3|13 low Small-Large isolated
## 2439 5 20 1013 3|13 low Small-Large isolated
## 2440 5 20 1013 3|13 low Small-Large isolated
## 2441 5 20 1013 3|13 low Small-Large isolated
## 2442 5 20 1013 3|13 low Small-Large isolated
## 2443 5 20 1013 3|13 low Small-Large isolated
## 2444 5 20 1013 3|13 low Small-Large isolated
## 2445 5 20 1013 3|13 low Small-Large isolated
## 2446 5 20 1013 3|13 low Small-Large isolated
## 2447 5 20 1013 3|13 low Small-Large isolated
## 2448 5 20 1013 3|13 low Small-Large isolated
## 2449 6 24 1013 3|13 low Small-Large isolated
## 2450 6 24 1013 3|13 low Small-Large isolated
## 2451 6 24 1013 3|13 low Small-Large isolated
## 2452 6 24 1013 3|13 low Small-Large isolated
## 2453 6 24 1013 3|13 low Small-Large isolated
## 2454 6 24 1013 3|13 low Small-Large isolated
## 2455 6 24 1013 3|13 low Small-Large isolated
## 2456 6 24 1013 3|13 low Small-Large isolated
## 2457 6 24 1013 3|13 low Small-Large isolated
## 2458 6 24 1013 3|13 low Small-Large isolated
## 2459 6 24 1013 3|13 low Small-Large isolated
## 2460 6 24 1013 3|13 low Small-Large isolated
## 2461 6 24 1013 3|13 low Small-Large isolated
## 2462 6 24 1013 3|13 low Small-Large isolated
## 2463 6 24 1013 3|13 low Small-Large isolated
## 2464 6 24 1013 3|13 low Small-Large isolated
## 2465 6 24 1013 3|13 low Small-Large isolated
## 2466 6 24 1013 3|13 low Small-Large isolated
## 2467 6 24 1013 3|13 low Small-Large isolated
## 2468 6 24 1013 3|13 low Small-Large isolated
## 2469 6 24 1013 3|13 low Small-Large isolated
## 2470 6 24 1013 3|13 low Small-Large isolated
## 2471 6 24 1013 3|13 low Small-Large isolated
## 2472 6 24 1013 3|13 low Small-Large isolated
## 2473 7 28 1013 3|13 low Small-Large isolated
## 2474 7 28 1013 3|13 low Small-Large isolated
## 2475 7 28 1013 3|13 low Small-Large isolated
## 2476 7 28 1013 3|13 low Small-Large isolated
## 2477 7 28 1013 3|13 low Small-Large isolated
## 2478 7 28 1013 3|13 low Small-Large isolated
## 2479 7 28 1013 3|13 low Small-Large isolated
## 2480 7 28 1013 3|13 low Small-Large isolated
## 2481 7 28 1013 3|13 low Small-Large isolated
## 2482 7 28 1013 3|13 low Small-Large isolated
## 2483 7 28 1013 3|13 low Small-Large isolated
## 2484 7 28 1013 3|13 low Small-Large isolated
## 2485 7 28 1013 3|13 low Small-Large isolated
## 2486 7 28 1013 3|13 low Small-Large isolated
## 2487 7 28 1013 3|13 low Small-Large isolated
## 2488 7 28 1013 3|13 low Small-Large isolated
## 2489 7 28 1013 3|13 low Small-Large isolated
## 2490 7 28 1013 3|13 low Small-Large isolated
## 2491 7 28 1013 3|13 low Small-Large isolated
## 2492 7 28 1013 3|13 low Small-Large isolated
## 2493 7 28 1013 3|13 low Small-Large isolated
## 2494 7 28 1013 3|13 low Small-Large isolated
## 2495 7 28 1013 3|13 low Small-Large isolated
## 2496 7 28 1013 3|13 low Small-Large isolated
## 2497 0 0 1014 3|14 low Small-Large isolated
## 2498 0 0 1014 3|14 low Small-Large isolated
## 2499 0 0 1014 3|14 low Small-Large isolated
## 2500 0 0 1014 3|14 low Small-Large isolated
## 2501 0 0 1014 3|14 low Small-Large isolated
## 2502 0 0 1014 3|14 low Small-Large isolated
## 2503 0 0 1014 3|14 low Small-Large isolated
## 2504 0 0 1014 3|14 low Small-Large isolated
## 2505 0 0 1014 3|14 low Small-Large isolated
## 2506 0 0 1014 3|14 low Small-Large isolated
## 2507 0 0 1014 3|14 low Small-Large isolated
## 2508 0 0 1014 3|14 low Small-Large isolated
## 2509 0 0 1014 3|14 low Small-Large isolated
## 2510 0 0 1014 3|14 low Small-Large isolated
## 2511 0 0 1014 3|14 low Small-Large isolated
## 2512 0 0 1014 3|14 low Small-Large isolated
## 2513 0 0 1014 3|14 low Small-Large isolated
## 2514 0 0 1014 3|14 low Small-Large isolated
## 2515 0 0 1014 3|14 low Small-Large isolated
## 2516 0 0 1014 3|14 low Small-Large isolated
## 2517 0 0 1014 3|14 low Small-Large isolated
## 2518 0 0 1014 3|14 low Small-Large isolated
## 2519 0 0 1014 3|14 low Small-Large isolated
## 2520 0 0 1014 3|14 low Small-Large isolated
## 2521 1 4 1014 3|14 low Small-Large isolated
## 2522 1 4 1014 3|14 low Small-Large isolated
## 2523 1 4 1014 3|14 low Small-Large isolated
## 2524 1 4 1014 3|14 low Small-Large isolated
## 2525 1 4 1014 3|14 low Small-Large isolated
## 2526 1 4 1014 3|14 low Small-Large isolated
## 2527 1 4 1014 3|14 low Small-Large isolated
## 2528 1 4 1014 3|14 low Small-Large isolated
## 2529 1 4 1014 3|14 low Small-Large isolated
## 2530 1 4 1014 3|14 low Small-Large isolated
## 2531 1 4 1014 3|14 low Small-Large isolated
## 2532 1 4 1014 3|14 low Small-Large isolated
## 2533 1 4 1014 3|14 low Small-Large isolated
## 2534 1 4 1014 3|14 low Small-Large isolated
## 2535 1 4 1014 3|14 low Small-Large isolated
## 2536 1 4 1014 3|14 low Small-Large isolated
## 2537 1 4 1014 3|14 low Small-Large isolated
## 2538 1 4 1014 3|14 low Small-Large isolated
## 2539 1 4 1014 3|14 low Small-Large isolated
## 2540 1 4 1014 3|14 low Small-Large isolated
## 2541 1 4 1014 3|14 low Small-Large isolated
## 2542 1 4 1014 3|14 low Small-Large isolated
## 2543 1 4 1014 3|14 low Small-Large isolated
## 2544 1 4 1014 3|14 low Small-Large isolated
## 2545 2 8 1014 3|14 low Small-Large isolated
## 2546 2 8 1014 3|14 low Small-Large isolated
## 2547 2 8 1014 3|14 low Small-Large isolated
## 2548 2 8 1014 3|14 low Small-Large isolated
## 2549 2 8 1014 3|14 low Small-Large isolated
## 2550 2 8 1014 3|14 low Small-Large isolated
## 2551 2 8 1014 3|14 low Small-Large isolated
## 2552 2 8 1014 3|14 low Small-Large isolated
## 2553 2 8 1014 3|14 low Small-Large isolated
## 2554 2 8 1014 3|14 low Small-Large isolated
## 2555 2 8 1014 3|14 low Small-Large isolated
## 2556 2 8 1014 3|14 low Small-Large isolated
## 2557 2 8 1014 3|14 low Small-Large isolated
## 2558 2 8 1014 3|14 low Small-Large isolated
## 2559 2 8 1014 3|14 low Small-Large isolated
## 2560 2 8 1014 3|14 low Small-Large isolated
## 2561 2 8 1014 3|14 low Small-Large isolated
## 2562 2 8 1014 3|14 low Small-Large isolated
## 2563 2 8 1014 3|14 low Small-Large isolated
## 2564 2 8 1014 3|14 low Small-Large isolated
## 2565 2 8 1014 3|14 low Small-Large isolated
## 2566 2 8 1014 3|14 low Small-Large isolated
## 2567 2 8 1014 3|14 low Small-Large isolated
## 2568 2 8 1014 3|14 low Small-Large isolated
## 2569 3 12 1014 3|14 low Small-Large isolated
## 2570 3 12 1014 3|14 low Small-Large isolated
## 2571 3 12 1014 3|14 low Small-Large isolated
## 2572 3 12 1014 3|14 low Small-Large isolated
## 2573 3 12 1014 3|14 low Small-Large isolated
## 2574 3 12 1014 3|14 low Small-Large isolated
## 2575 3 12 1014 3|14 low Small-Large isolated
## 2576 3 12 1014 3|14 low Small-Large isolated
## 2577 3 12 1014 3|14 low Small-Large isolated
## 2578 3 12 1014 3|14 low Small-Large isolated
## 2579 3 12 1014 3|14 low Small-Large isolated
## 2580 3 12 1014 3|14 low Small-Large isolated
## 2581 3 12 1014 3|14 low Small-Large isolated
## 2582 3 12 1014 3|14 low Small-Large isolated
## 2583 3 12 1014 3|14 low Small-Large isolated
## 2584 3 12 1014 3|14 low Small-Large isolated
## 2585 3 12 1014 3|14 low Small-Large isolated
## 2586 3 12 1014 3|14 low Small-Large isolated
## 2587 3 12 1014 3|14 low Small-Large isolated
## 2588 3 12 1014 3|14 low Small-Large isolated
## 2589 3 12 1014 3|14 low Small-Large isolated
## 2590 3 12 1014 3|14 low Small-Large isolated
## 2591 3 12 1014 3|14 low Small-Large isolated
## 2592 3 12 1014 3|14 low Small-Large isolated
## 2593 4 16 1014 3|14 low Small-Large isolated
## 2594 4 16 1014 3|14 low Small-Large isolated
## 2595 4 16 1014 3|14 low Small-Large isolated
## 2596 4 16 1014 3|14 low Small-Large isolated
## 2597 4 16 1014 3|14 low Small-Large isolated
## 2598 4 16 1014 3|14 low Small-Large isolated
## 2599 4 16 1014 3|14 low Small-Large isolated
## 2600 4 16 1014 3|14 low Small-Large isolated
## 2601 4 16 1014 3|14 low Small-Large isolated
## 2602 4 16 1014 3|14 low Small-Large isolated
## 2603 4 16 1014 3|14 low Small-Large isolated
## 2604 4 16 1014 3|14 low Small-Large isolated
## 2605 4 16 1014 3|14 low Small-Large isolated
## 2606 4 16 1014 3|14 low Small-Large isolated
## 2607 4 16 1014 3|14 low Small-Large isolated
## 2608 4 16 1014 3|14 low Small-Large isolated
## 2609 4 16 1014 3|14 low Small-Large isolated
## 2610 4 16 1014 3|14 low Small-Large isolated
## 2611 4 16 1014 3|14 low Small-Large isolated
## 2612 4 16 1014 3|14 low Small-Large isolated
## 2613 4 16 1014 3|14 low Small-Large isolated
## 2614 4 16 1014 3|14 low Small-Large isolated
## 2615 4 16 1014 3|14 low Small-Large isolated
## 2616 4 16 1014 3|14 low Small-Large isolated
## 2617 5 20 1014 3|14 low Small-Large isolated
## 2618 5 20 1014 3|14 low Small-Large isolated
## 2619 5 20 1014 3|14 low Small-Large isolated
## 2620 5 20 1014 3|14 low Small-Large isolated
## 2621 5 20 1014 3|14 low Small-Large isolated
## 2622 5 20 1014 3|14 low Small-Large isolated
## 2623 5 20 1014 3|14 low Small-Large isolated
## 2624 5 20 1014 3|14 low Small-Large isolated
## 2625 5 20 1014 3|14 low Small-Large isolated
## 2626 5 20 1014 3|14 low Small-Large isolated
## 2627 5 20 1014 3|14 low Small-Large isolated
## 2628 5 20 1014 3|14 low Small-Large isolated
## 2629 5 20 1014 3|14 low Small-Large isolated
## 2630 5 20 1014 3|14 low Small-Large isolated
## 2631 5 20 1014 3|14 low Small-Large isolated
## 2632 5 20 1014 3|14 low Small-Large isolated
## 2633 5 20 1014 3|14 low Small-Large isolated
## 2634 5 20 1014 3|14 low Small-Large isolated
## 2635 5 20 1014 3|14 low Small-Large isolated
## 2636 5 20 1014 3|14 low Small-Large isolated
## 2637 5 20 1014 3|14 low Small-Large isolated
## 2638 5 20 1014 3|14 low Small-Large isolated
## 2639 5 20 1014 3|14 low Small-Large isolated
## 2640 5 20 1014 3|14 low Small-Large isolated
## 2641 6 24 1014 3|14 low Small-Large isolated
## 2642 6 24 1014 3|14 low Small-Large isolated
## 2643 6 24 1014 3|14 low Small-Large isolated
## 2644 6 24 1014 3|14 low Small-Large isolated
## 2645 6 24 1014 3|14 low Small-Large isolated
## 2646 6 24 1014 3|14 low Small-Large isolated
## 2647 6 24 1014 3|14 low Small-Large isolated
## 2648 6 24 1014 3|14 low Small-Large isolated
## 2649 6 24 1014 3|14 low Small-Large isolated
## 2650 6 24 1014 3|14 low Small-Large isolated
## 2651 6 24 1014 3|14 low Small-Large isolated
## 2652 6 24 1014 3|14 low Small-Large isolated
## 2653 6 24 1014 3|14 low Small-Large isolated
## 2654 6 24 1014 3|14 low Small-Large isolated
## 2655 6 24 1014 3|14 low Small-Large isolated
## 2656 6 24 1014 3|14 low Small-Large isolated
## 2657 6 24 1014 3|14 low Small-Large isolated
## 2658 6 24 1014 3|14 low Small-Large isolated
## 2659 6 24 1014 3|14 low Small-Large isolated
## 2660 6 24 1014 3|14 low Small-Large isolated
## 2661 6 24 1014 3|14 low Small-Large isolated
## 2662 6 24 1014 3|14 low Small-Large isolated
## 2663 6 24 1014 3|14 low Small-Large isolated
## 2664 6 24 1014 3|14 low Small-Large isolated
## 2665 7 28 1014 3|14 low Small-Large isolated
## 2666 7 28 1014 3|14 low Small-Large isolated
## 2667 7 28 1014 3|14 low Small-Large isolated
## 2668 7 28 1014 3|14 low Small-Large isolated
## 2669 7 28 1014 3|14 low Small-Large isolated
## 2670 7 28 1014 3|14 low Small-Large isolated
## 2671 7 28 1014 3|14 low Small-Large isolated
## 2672 7 28 1014 3|14 low Small-Large isolated
## 2673 7 28 1014 3|14 low Small-Large isolated
## 2674 7 28 1014 3|14 low Small-Large isolated
## 2675 7 28 1014 3|14 low Small-Large isolated
## 2676 7 28 1014 3|14 low Small-Large isolated
## 2677 7 28 1014 3|14 low Small-Large isolated
## 2678 7 28 1014 3|14 low Small-Large isolated
## 2679 7 28 1014 3|14 low Small-Large isolated
## 2680 7 28 1014 3|14 low Small-Large isolated
## 2681 7 28 1014 3|14 low Small-Large isolated
## 2682 7 28 1014 3|14 low Small-Large isolated
## 2683 7 28 1014 3|14 low Small-Large isolated
## 2684 7 28 1014 3|14 low Small-Large isolated
## 2685 7 28 1014 3|14 low Small-Large isolated
## 2686 7 28 1014 3|14 low Small-Large isolated
## 2687 7 28 1014 3|14 low Small-Large isolated
## 2688 7 28 1014 3|14 low Small-Large isolated
## 2689 0 0 1015 3|15 low Small-Large isolated
## 2690 0 0 1015 3|15 low Small-Large isolated
## 2691 0 0 1015 3|15 low Small-Large isolated
## 2692 0 0 1015 3|15 low Small-Large isolated
## 2693 0 0 1015 3|15 low Small-Large isolated
## 2694 0 0 1015 3|15 low Small-Large isolated
## 2695 0 0 1015 3|15 low Small-Large isolated
## 2696 0 0 1015 3|15 low Small-Large isolated
## 2697 0 0 1015 3|15 low Small-Large isolated
## 2698 0 0 1015 3|15 low Small-Large isolated
## 2699 0 0 1015 3|15 low Small-Large isolated
## 2700 0 0 1015 3|15 low Small-Large isolated
## 2701 0 0 1015 3|15 low Small-Large isolated
## 2702 0 0 1015 3|15 low Small-Large isolated
## 2703 0 0 1015 3|15 low Small-Large isolated
## 2704 0 0 1015 3|15 low Small-Large isolated
## 2705 0 0 1015 3|15 low Small-Large isolated
## 2706 0 0 1015 3|15 low Small-Large isolated
## 2707 0 0 1015 3|15 low Small-Large isolated
## 2708 0 0 1015 3|15 low Small-Large isolated
## 2709 0 0 1015 3|15 low Small-Large isolated
## 2710 0 0 1015 3|15 low Small-Large isolated
## 2711 0 0 1015 3|15 low Small-Large isolated
## 2712 0 0 1015 3|15 low Small-Large isolated
## 2713 1 4 1015 3|15 low Small-Large isolated
## 2714 1 4 1015 3|15 low Small-Large isolated
## 2715 1 4 1015 3|15 low Small-Large isolated
## 2716 1 4 1015 3|15 low Small-Large isolated
## 2717 1 4 1015 3|15 low Small-Large isolated
## 2718 1 4 1015 3|15 low Small-Large isolated
## 2719 1 4 1015 3|15 low Small-Large isolated
## 2720 1 4 1015 3|15 low Small-Large isolated
## 2721 1 4 1015 3|15 low Small-Large isolated
## 2722 1 4 1015 3|15 low Small-Large isolated
## 2723 1 4 1015 3|15 low Small-Large isolated
## 2724 1 4 1015 3|15 low Small-Large isolated
## 2725 1 4 1015 3|15 low Small-Large isolated
## 2726 1 4 1015 3|15 low Small-Large isolated
## 2727 1 4 1015 3|15 low Small-Large isolated
## 2728 1 4 1015 3|15 low Small-Large isolated
## 2729 1 4 1015 3|15 low Small-Large isolated
## 2730 1 4 1015 3|15 low Small-Large isolated
## 2731 1 4 1015 3|15 low Small-Large isolated
## 2732 1 4 1015 3|15 low Small-Large isolated
## 2733 1 4 1015 3|15 low Small-Large isolated
## 2734 1 4 1015 3|15 low Small-Large isolated
## 2735 1 4 1015 3|15 low Small-Large isolated
## 2736 1 4 1015 3|15 low Small-Large isolated
## 2737 2 8 1015 3|15 low Small-Large isolated
## 2738 2 8 1015 3|15 low Small-Large isolated
## 2739 2 8 1015 3|15 low Small-Large isolated
## 2740 2 8 1015 3|15 low Small-Large isolated
## 2741 2 8 1015 3|15 low Small-Large isolated
## 2742 2 8 1015 3|15 low Small-Large isolated
## 2743 2 8 1015 3|15 low Small-Large isolated
## 2744 2 8 1015 3|15 low Small-Large isolated
## 2745 2 8 1015 3|15 low Small-Large isolated
## 2746 2 8 1015 3|15 low Small-Large isolated
## 2747 2 8 1015 3|15 low Small-Large isolated
## 2748 2 8 1015 3|15 low Small-Large isolated
## 2749 2 8 1015 3|15 low Small-Large isolated
## 2750 2 8 1015 3|15 low Small-Large isolated
## 2751 2 8 1015 3|15 low Small-Large isolated
## 2752 2 8 1015 3|15 low Small-Large isolated
## 2753 2 8 1015 3|15 low Small-Large isolated
## 2754 2 8 1015 3|15 low Small-Large isolated
## 2755 2 8 1015 3|15 low Small-Large isolated
## 2756 2 8 1015 3|15 low Small-Large isolated
## 2757 2 8 1015 3|15 low Small-Large isolated
## 2758 2 8 1015 3|15 low Small-Large isolated
## 2759 2 8 1015 3|15 low Small-Large isolated
## 2760 2 8 1015 3|15 low Small-Large isolated
## 2761 3 12 1015 3|15 low Small-Large isolated
## 2762 3 12 1015 3|15 low Small-Large isolated
## 2763 3 12 1015 3|15 low Small-Large isolated
## 2764 3 12 1015 3|15 low Small-Large isolated
## 2765 3 12 1015 3|15 low Small-Large isolated
## 2766 3 12 1015 3|15 low Small-Large isolated
## 2767 3 12 1015 3|15 low Small-Large isolated
## 2768 3 12 1015 3|15 low Small-Large isolated
## 2769 3 12 1015 3|15 low Small-Large isolated
## 2770 3 12 1015 3|15 low Small-Large isolated
## 2771 3 12 1015 3|15 low Small-Large isolated
## 2772 3 12 1015 3|15 low Small-Large isolated
## 2773 3 12 1015 3|15 low Small-Large isolated
## 2774 3 12 1015 3|15 low Small-Large isolated
## 2775 3 12 1015 3|15 low Small-Large isolated
## 2776 3 12 1015 3|15 low Small-Large isolated
## 2777 3 12 1015 3|15 low Small-Large isolated
## 2778 3 12 1015 3|15 low Small-Large isolated
## 2779 3 12 1015 3|15 low Small-Large isolated
## 2780 3 12 1015 3|15 low Small-Large isolated
## 2781 3 12 1015 3|15 low Small-Large isolated
## 2782 3 12 1015 3|15 low Small-Large isolated
## 2783 3 12 1015 3|15 low Small-Large isolated
## 2784 3 12 1015 3|15 low Small-Large isolated
## 2785 4 16 1015 3|15 low Small-Large isolated
## 2786 4 16 1015 3|15 low Small-Large isolated
## 2787 4 16 1015 3|15 low Small-Large isolated
## 2788 4 16 1015 3|15 low Small-Large isolated
## 2789 4 16 1015 3|15 low Small-Large isolated
## 2790 4 16 1015 3|15 low Small-Large isolated
## 2791 4 16 1015 3|15 low Small-Large isolated
## 2792 4 16 1015 3|15 low Small-Large isolated
## 2793 4 16 1015 3|15 low Small-Large isolated
## 2794 4 16 1015 3|15 low Small-Large isolated
## 2795 4 16 1015 3|15 low Small-Large isolated
## 2796 4 16 1015 3|15 low Small-Large isolated
## 2797 4 16 1015 3|15 low Small-Large isolated
## 2798 4 16 1015 3|15 low Small-Large isolated
## 2799 4 16 1015 3|15 low Small-Large isolated
## 2800 4 16 1015 3|15 low Small-Large isolated
## 2801 4 16 1015 3|15 low Small-Large isolated
## 2802 4 16 1015 3|15 low Small-Large isolated
## 2803 4 16 1015 3|15 low Small-Large isolated
## 2804 4 16 1015 3|15 low Small-Large isolated
## 2805 4 16 1015 3|15 low Small-Large isolated
## 2806 4 16 1015 3|15 low Small-Large isolated
## 2807 4 16 1015 3|15 low Small-Large isolated
## 2808 4 16 1015 3|15 low Small-Large isolated
## 2809 5 20 1015 3|15 low Small-Large isolated
## 2810 5 20 1015 3|15 low Small-Large isolated
## 2811 5 20 1015 3|15 low Small-Large isolated
## 2812 5 20 1015 3|15 low Small-Large isolated
## 2813 5 20 1015 3|15 low Small-Large isolated
## 2814 5 20 1015 3|15 low Small-Large isolated
## 2815 5 20 1015 3|15 low Small-Large isolated
## 2816 5 20 1015 3|15 low Small-Large isolated
## 2817 5 20 1015 3|15 low Small-Large isolated
## 2818 5 20 1015 3|15 low Small-Large isolated
## 2819 5 20 1015 3|15 low Small-Large isolated
## 2820 5 20 1015 3|15 low Small-Large isolated
## 2821 5 20 1015 3|15 low Small-Large isolated
## 2822 5 20 1015 3|15 low Small-Large isolated
## 2823 5 20 1015 3|15 low Small-Large isolated
## 2824 5 20 1015 3|15 low Small-Large isolated
## 2825 5 20 1015 3|15 low Small-Large isolated
## 2826 5 20 1015 3|15 low Small-Large isolated
## 2827 5 20 1015 3|15 low Small-Large isolated
## 2828 5 20 1015 3|15 low Small-Large isolated
## 2829 5 20 1015 3|15 low Small-Large isolated
## 2830 5 20 1015 3|15 low Small-Large isolated
## 2831 5 20 1015 3|15 low Small-Large isolated
## 2832 5 20 1015 3|15 low Small-Large isolated
## 2833 6 24 1015 3|15 low Small-Large isolated
## 2834 6 24 1015 3|15 low Small-Large isolated
## 2835 6 24 1015 3|15 low Small-Large isolated
## 2836 6 24 1015 3|15 low Small-Large isolated
## 2837 6 24 1015 3|15 low Small-Large isolated
## 2838 6 24 1015 3|15 low Small-Large isolated
## 2839 6 24 1015 3|15 low Small-Large isolated
## 2840 6 24 1015 3|15 low Small-Large isolated
## 2841 6 24 1015 3|15 low Small-Large isolated
## 2842 6 24 1015 3|15 low Small-Large isolated
## 2843 6 24 1015 3|15 low Small-Large isolated
## 2844 6 24 1015 3|15 low Small-Large isolated
## 2845 6 24 1015 3|15 low Small-Large isolated
## 2846 6 24 1015 3|15 low Small-Large isolated
## 2847 6 24 1015 3|15 low Small-Large isolated
## 2848 6 24 1015 3|15 low Small-Large isolated
## 2849 6 24 1015 3|15 low Small-Large isolated
## 2850 6 24 1015 3|15 low Small-Large isolated
## 2851 6 24 1015 3|15 low Small-Large isolated
## 2852 6 24 1015 3|15 low Small-Large isolated
## 2853 6 24 1015 3|15 low Small-Large isolated
## 2854 6 24 1015 3|15 low Small-Large isolated
## 2855 6 24 1015 3|15 low Small-Large isolated
## 2856 6 24 1015 3|15 low Small-Large isolated
## 2857 7 28 1015 3|15 low Small-Large isolated
## 2858 7 28 1015 3|15 low Small-Large isolated
## 2859 7 28 1015 3|15 low Small-Large isolated
## 2860 7 28 1015 3|15 low Small-Large isolated
## 2861 7 28 1015 3|15 low Small-Large isolated
## 2862 7 28 1015 3|15 low Small-Large isolated
## 2863 7 28 1015 3|15 low Small-Large isolated
## 2864 7 28 1015 3|15 low Small-Large isolated
## 2865 7 28 1015 3|15 low Small-Large isolated
## 2866 7 28 1015 3|15 low Small-Large isolated
## 2867 7 28 1015 3|15 low Small-Large isolated
## 2868 7 28 1015 3|15 low Small-Large isolated
## 2869 7 28 1015 3|15 low Small-Large isolated
## 2870 7 28 1015 3|15 low Small-Large isolated
## 2871 7 28 1015 3|15 low Small-Large isolated
## 2872 7 28 1015 3|15 low Small-Large isolated
## 2873 7 28 1015 3|15 low Small-Large isolated
## 2874 7 28 1015 3|15 low Small-Large isolated
## 2875 7 28 1015 3|15 low Small-Large isolated
## 2876 7 28 1015 3|15 low Small-Large isolated
## 2877 7 28 1015 3|15 low Small-Large isolated
## 2878 7 28 1015 3|15 low Small-Large isolated
## 2879 7 28 1015 3|15 low Small-Large isolated
## 2880 7 28 1015 3|15 low Small-Large isolated
## 2881 0 0 1016 4|11 low Small-Large isolated
## 2882 0 0 1016 4|11 low Small-Large isolated
## 2883 0 0 1016 4|11 low Small-Large isolated
## 2884 0 0 1016 4|11 low Small-Large isolated
## 2885 0 0 1016 4|11 low Small-Large isolated
## 2886 0 0 1016 4|11 low Small-Large isolated
## 2887 0 0 1016 4|11 low Small-Large isolated
## 2888 0 0 1016 4|11 low Small-Large isolated
## 2889 0 0 1016 4|11 low Small-Large isolated
## 2890 0 0 1016 4|11 low Small-Large isolated
## 2891 0 0 1016 4|11 low Small-Large isolated
## 2892 0 0 1016 4|11 low Small-Large isolated
## 2893 0 0 1016 4|11 low Small-Large isolated
## 2894 0 0 1016 4|11 low Small-Large isolated
## 2895 0 0 1016 4|11 low Small-Large isolated
## 2896 0 0 1016 4|11 low Small-Large isolated
## 2897 0 0 1016 4|11 low Small-Large isolated
## 2898 0 0 1016 4|11 low Small-Large isolated
## 2899 0 0 1016 4|11 low Small-Large isolated
## 2900 0 0 1016 4|11 low Small-Large isolated
## 2901 0 0 1016 4|11 low Small-Large isolated
## 2902 0 0 1016 4|11 low Small-Large isolated
## 2903 0 0 1016 4|11 low Small-Large isolated
## 2904 0 0 1016 4|11 low Small-Large isolated
## 2905 1 4 1016 4|11 low Small-Large isolated
## 2906 1 4 1016 4|11 low Small-Large isolated
## 2907 1 4 1016 4|11 low Small-Large isolated
## 2908 1 4 1016 4|11 low Small-Large isolated
## 2909 1 4 1016 4|11 low Small-Large isolated
## 2910 1 4 1016 4|11 low Small-Large isolated
## 2911 1 4 1016 4|11 low Small-Large isolated
## 2912 1 4 1016 4|11 low Small-Large isolated
## 2913 1 4 1016 4|11 low Small-Large isolated
## 2914 1 4 1016 4|11 low Small-Large isolated
## 2915 1 4 1016 4|11 low Small-Large isolated
## 2916 1 4 1016 4|11 low Small-Large isolated
## 2917 1 4 1016 4|11 low Small-Large isolated
## 2918 1 4 1016 4|11 low Small-Large isolated
## 2919 1 4 1016 4|11 low Small-Large isolated
## 2920 1 4 1016 4|11 low Small-Large isolated
## 2921 1 4 1016 4|11 low Small-Large isolated
## 2922 1 4 1016 4|11 low Small-Large isolated
## 2923 1 4 1016 4|11 low Small-Large isolated
## 2924 1 4 1016 4|11 low Small-Large isolated
## 2925 1 4 1016 4|11 low Small-Large isolated
## 2926 1 4 1016 4|11 low Small-Large isolated
## 2927 1 4 1016 4|11 low Small-Large isolated
## 2928 1 4 1016 4|11 low Small-Large isolated
## 2929 2 8 1016 4|11 low Small-Large isolated
## 2930 2 8 1016 4|11 low Small-Large isolated
## 2931 2 8 1016 4|11 low Small-Large isolated
## 2932 2 8 1016 4|11 low Small-Large isolated
## 2933 2 8 1016 4|11 low Small-Large isolated
## 2934 2 8 1016 4|11 low Small-Large isolated
## 2935 2 8 1016 4|11 low Small-Large isolated
## 2936 2 8 1016 4|11 low Small-Large isolated
## 2937 2 8 1016 4|11 low Small-Large isolated
## 2938 2 8 1016 4|11 low Small-Large isolated
## 2939 2 8 1016 4|11 low Small-Large isolated
## 2940 2 8 1016 4|11 low Small-Large isolated
## 2941 2 8 1016 4|11 low Small-Large isolated
## 2942 2 8 1016 4|11 low Small-Large isolated
## 2943 2 8 1016 4|11 low Small-Large isolated
## 2944 2 8 1016 4|11 low Small-Large isolated
## 2945 2 8 1016 4|11 low Small-Large isolated
## 2946 2 8 1016 4|11 low Small-Large isolated
## 2947 2 8 1016 4|11 low Small-Large isolated
## 2948 2 8 1016 4|11 low Small-Large isolated
## 2949 2 8 1016 4|11 low Small-Large isolated
## 2950 2 8 1016 4|11 low Small-Large isolated
## 2951 2 8 1016 4|11 low Small-Large isolated
## 2952 2 8 1016 4|11 low Small-Large isolated
## 2953 3 12 1016 4|11 low Small-Large isolated
## 2954 3 12 1016 4|11 low Small-Large isolated
## 2955 3 12 1016 4|11 low Small-Large isolated
## 2956 3 12 1016 4|11 low Small-Large isolated
## 2957 3 12 1016 4|11 low Small-Large isolated
## 2958 3 12 1016 4|11 low Small-Large isolated
## 2959 3 12 1016 4|11 low Small-Large isolated
## 2960 3 12 1016 4|11 low Small-Large isolated
## 2961 3 12 1016 4|11 low Small-Large isolated
## 2962 3 12 1016 4|11 low Small-Large isolated
## 2963 3 12 1016 4|11 low Small-Large isolated
## 2964 3 12 1016 4|11 low Small-Large isolated
## 2965 3 12 1016 4|11 low Small-Large isolated
## 2966 3 12 1016 4|11 low Small-Large isolated
## 2967 3 12 1016 4|11 low Small-Large isolated
## 2968 3 12 1016 4|11 low Small-Large isolated
## 2969 3 12 1016 4|11 low Small-Large isolated
## 2970 3 12 1016 4|11 low Small-Large isolated
## 2971 3 12 1016 4|11 low Small-Large isolated
## 2972 3 12 1016 4|11 low Small-Large isolated
## 2973 3 12 1016 4|11 low Small-Large isolated
## 2974 3 12 1016 4|11 low Small-Large isolated
## 2975 3 12 1016 4|11 low Small-Large isolated
## 2976 3 12 1016 4|11 low Small-Large isolated
## 2977 4 16 1016 4|11 low Small-Large isolated
## 2978 4 16 1016 4|11 low Small-Large isolated
## 2979 4 16 1016 4|11 low Small-Large isolated
## 2980 4 16 1016 4|11 low Small-Large isolated
## 2981 4 16 1016 4|11 low Small-Large isolated
## 2982 4 16 1016 4|11 low Small-Large isolated
## 2983 4 16 1016 4|11 low Small-Large isolated
## 2984 4 16 1016 4|11 low Small-Large isolated
## 2985 4 16 1016 4|11 low Small-Large isolated
## 2986 4 16 1016 4|11 low Small-Large isolated
## 2987 4 16 1016 4|11 low Small-Large isolated
## 2988 4 16 1016 4|11 low Small-Large isolated
## 2989 4 16 1016 4|11 low Small-Large isolated
## 2990 4 16 1016 4|11 low Small-Large isolated
## 2991 4 16 1016 4|11 low Small-Large isolated
## 2992 4 16 1016 4|11 low Small-Large isolated
## 2993 4 16 1016 4|11 low Small-Large isolated
## 2994 4 16 1016 4|11 low Small-Large isolated
## 2995 4 16 1016 4|11 low Small-Large isolated
## 2996 4 16 1016 4|11 low Small-Large isolated
## 2997 4 16 1016 4|11 low Small-Large isolated
## 2998 4 16 1016 4|11 low Small-Large isolated
## 2999 4 16 1016 4|11 low Small-Large isolated
## 3000 4 16 1016 4|11 low Small-Large isolated
## 3001 5 20 1016 4|11 low Small-Large isolated
## 3002 5 20 1016 4|11 low Small-Large isolated
## 3003 5 20 1016 4|11 low Small-Large isolated
## 3004 5 20 1016 4|11 low Small-Large isolated
## 3005 5 20 1016 4|11 low Small-Large isolated
## 3006 5 20 1016 4|11 low Small-Large isolated
## 3007 5 20 1016 4|11 low Small-Large isolated
## 3008 5 20 1016 4|11 low Small-Large isolated
## 3009 5 20 1016 4|11 low Small-Large isolated
## 3010 5 20 1016 4|11 low Small-Large isolated
## 3011 5 20 1016 4|11 low Small-Large isolated
## 3012 5 20 1016 4|11 low Small-Large isolated
## 3013 5 20 1016 4|11 low Small-Large isolated
## 3014 5 20 1016 4|11 low Small-Large isolated
## 3015 5 20 1016 4|11 low Small-Large isolated
## 3016 5 20 1016 4|11 low Small-Large isolated
## 3017 5 20 1016 4|11 low Small-Large isolated
## 3018 5 20 1016 4|11 low Small-Large isolated
## 3019 5 20 1016 4|11 low Small-Large isolated
## 3020 5 20 1016 4|11 low Small-Large isolated
## 3021 5 20 1016 4|11 low Small-Large isolated
## 3022 5 20 1016 4|11 low Small-Large isolated
## 3023 5 20 1016 4|11 low Small-Large isolated
## 3024 5 20 1016 4|11 low Small-Large isolated
## 3025 6 24 1016 4|11 low Small-Large isolated
## 3026 6 24 1016 4|11 low Small-Large isolated
## 3027 6 24 1016 4|11 low Small-Large isolated
## 3028 6 24 1016 4|11 low Small-Large isolated
## 3029 6 24 1016 4|11 low Small-Large isolated
## 3030 6 24 1016 4|11 low Small-Large isolated
## 3031 6 24 1016 4|11 low Small-Large isolated
## 3032 6 24 1016 4|11 low Small-Large isolated
## 3033 6 24 1016 4|11 low Small-Large isolated
## 3034 6 24 1016 4|11 low Small-Large isolated
## 3035 6 24 1016 4|11 low Small-Large isolated
## 3036 6 24 1016 4|11 low Small-Large isolated
## 3037 6 24 1016 4|11 low Small-Large isolated
## 3038 6 24 1016 4|11 low Small-Large isolated
## 3039 6 24 1016 4|11 low Small-Large isolated
## 3040 6 24 1016 4|11 low Small-Large isolated
## 3041 6 24 1016 4|11 low Small-Large isolated
## 3042 6 24 1016 4|11 low Small-Large isolated
## 3043 6 24 1016 4|11 low Small-Large isolated
## 3044 6 24 1016 4|11 low Small-Large isolated
## 3045 6 24 1016 4|11 low Small-Large isolated
## 3046 6 24 1016 4|11 low Small-Large isolated
## 3047 6 24 1016 4|11 low Small-Large isolated
## 3048 6 24 1016 4|11 low Small-Large isolated
## 3049 7 28 1016 4|11 low Small-Large isolated
## 3050 7 28 1016 4|11 low Small-Large isolated
## 3051 7 28 1016 4|11 low Small-Large isolated
## 3052 7 28 1016 4|11 low Small-Large isolated
## 3053 7 28 1016 4|11 low Small-Large isolated
## 3054 7 28 1016 4|11 low Small-Large isolated
## 3055 7 28 1016 4|11 low Small-Large isolated
## 3056 7 28 1016 4|11 low Small-Large isolated
## 3057 7 28 1016 4|11 low Small-Large isolated
## 3058 7 28 1016 4|11 low Small-Large isolated
## 3059 7 28 1016 4|11 low Small-Large isolated
## 3060 7 28 1016 4|11 low Small-Large isolated
## 3061 7 28 1016 4|11 low Small-Large isolated
## 3062 7 28 1016 4|11 low Small-Large isolated
## 3063 7 28 1016 4|11 low Small-Large isolated
## 3064 7 28 1016 4|11 low Small-Large isolated
## 3065 7 28 1016 4|11 low Small-Large isolated
## 3066 7 28 1016 4|11 low Small-Large isolated
## 3067 7 28 1016 4|11 low Small-Large isolated
## 3068 7 28 1016 4|11 low Small-Large isolated
## 3069 7 28 1016 4|11 low Small-Large isolated
## 3070 7 28 1016 4|11 low Small-Large isolated
## 3071 7 28 1016 4|11 low Small-Large isolated
## 3072 7 28 1016 4|11 low Small-Large isolated
## 3073 0 0 1017 4|12 low Small-Large isolated
## 3074 0 0 1017 4|12 low Small-Large isolated
## 3075 0 0 1017 4|12 low Small-Large isolated
## 3076 0 0 1017 4|12 low Small-Large isolated
## 3077 0 0 1017 4|12 low Small-Large isolated
## 3078 0 0 1017 4|12 low Small-Large isolated
## 3079 0 0 1017 4|12 low Small-Large isolated
## 3080 0 0 1017 4|12 low Small-Large isolated
## 3081 0 0 1017 4|12 low Small-Large isolated
## 3082 0 0 1017 4|12 low Small-Large isolated
## 3083 0 0 1017 4|12 low Small-Large isolated
## 3084 0 0 1017 4|12 low Small-Large isolated
## 3085 0 0 1017 4|12 low Small-Large isolated
## 3086 0 0 1017 4|12 low Small-Large isolated
## 3087 0 0 1017 4|12 low Small-Large isolated
## 3088 0 0 1017 4|12 low Small-Large isolated
## 3089 0 0 1017 4|12 low Small-Large isolated
## 3090 0 0 1017 4|12 low Small-Large isolated
## 3091 0 0 1017 4|12 low Small-Large isolated
## 3092 0 0 1017 4|12 low Small-Large isolated
## 3093 0 0 1017 4|12 low Small-Large isolated
## 3094 0 0 1017 4|12 low Small-Large isolated
## 3095 0 0 1017 4|12 low Small-Large isolated
## 3096 0 0 1017 4|12 low Small-Large isolated
## 3097 1 4 1017 4|12 low Small-Large isolated
## 3098 1 4 1017 4|12 low Small-Large isolated
## 3099 1 4 1017 4|12 low Small-Large isolated
## 3100 1 4 1017 4|12 low Small-Large isolated
## 3101 1 4 1017 4|12 low Small-Large isolated
## 3102 1 4 1017 4|12 low Small-Large isolated
## 3103 1 4 1017 4|12 low Small-Large isolated
## 3104 1 4 1017 4|12 low Small-Large isolated
## 3105 1 4 1017 4|12 low Small-Large isolated
## 3106 1 4 1017 4|12 low Small-Large isolated
## 3107 1 4 1017 4|12 low Small-Large isolated
## 3108 1 4 1017 4|12 low Small-Large isolated
## 3109 1 4 1017 4|12 low Small-Large isolated
## 3110 1 4 1017 4|12 low Small-Large isolated
## 3111 1 4 1017 4|12 low Small-Large isolated
## 3112 1 4 1017 4|12 low Small-Large isolated
## 3113 1 4 1017 4|12 low Small-Large isolated
## 3114 1 4 1017 4|12 low Small-Large isolated
## 3115 1 4 1017 4|12 low Small-Large isolated
## 3116 1 4 1017 4|12 low Small-Large isolated
## 3117 1 4 1017 4|12 low Small-Large isolated
## 3118 1 4 1017 4|12 low Small-Large isolated
## 3119 1 4 1017 4|12 low Small-Large isolated
## 3120 1 4 1017 4|12 low Small-Large isolated
## 3121 2 8 1017 4|12 low Small-Large isolated
## 3122 2 8 1017 4|12 low Small-Large isolated
## 3123 2 8 1017 4|12 low Small-Large isolated
## 3124 2 8 1017 4|12 low Small-Large isolated
## 3125 2 8 1017 4|12 low Small-Large isolated
## 3126 2 8 1017 4|12 low Small-Large isolated
## 3127 2 8 1017 4|12 low Small-Large isolated
## 3128 2 8 1017 4|12 low Small-Large isolated
## 3129 2 8 1017 4|12 low Small-Large isolated
## 3130 2 8 1017 4|12 low Small-Large isolated
## 3131 2 8 1017 4|12 low Small-Large isolated
## 3132 2 8 1017 4|12 low Small-Large isolated
## 3133 2 8 1017 4|12 low Small-Large isolated
## 3134 2 8 1017 4|12 low Small-Large isolated
## 3135 2 8 1017 4|12 low Small-Large isolated
## 3136 2 8 1017 4|12 low Small-Large isolated
## 3137 2 8 1017 4|12 low Small-Large isolated
## 3138 2 8 1017 4|12 low Small-Large isolated
## 3139 2 8 1017 4|12 low Small-Large isolated
## 3140 2 8 1017 4|12 low Small-Large isolated
## 3141 2 8 1017 4|12 low Small-Large isolated
## 3142 2 8 1017 4|12 low Small-Large isolated
## 3143 2 8 1017 4|12 low Small-Large isolated
## 3144 2 8 1017 4|12 low Small-Large isolated
## 3145 3 12 1017 4|12 low Small-Large isolated
## 3146 3 12 1017 4|12 low Small-Large isolated
## 3147 3 12 1017 4|12 low Small-Large isolated
## 3148 3 12 1017 4|12 low Small-Large isolated
## 3149 3 12 1017 4|12 low Small-Large isolated
## 3150 3 12 1017 4|12 low Small-Large isolated
## 3151 3 12 1017 4|12 low Small-Large isolated
## 3152 3 12 1017 4|12 low Small-Large isolated
## 3153 3 12 1017 4|12 low Small-Large isolated
## 3154 3 12 1017 4|12 low Small-Large isolated
## 3155 3 12 1017 4|12 low Small-Large isolated
## 3156 3 12 1017 4|12 low Small-Large isolated
## 3157 3 12 1017 4|12 low Small-Large isolated
## 3158 3 12 1017 4|12 low Small-Large isolated
## 3159 3 12 1017 4|12 low Small-Large isolated
## 3160 3 12 1017 4|12 low Small-Large isolated
## 3161 3 12 1017 4|12 low Small-Large isolated
## 3162 3 12 1017 4|12 low Small-Large isolated
## 3163 3 12 1017 4|12 low Small-Large isolated
## 3164 3 12 1017 4|12 low Small-Large isolated
## 3165 3 12 1017 4|12 low Small-Large isolated
## 3166 3 12 1017 4|12 low Small-Large isolated
## 3167 3 12 1017 4|12 low Small-Large isolated
## 3168 3 12 1017 4|12 low Small-Large isolated
## 3169 4 16 1017 4|12 low Small-Large isolated
## 3170 4 16 1017 4|12 low Small-Large isolated
## 3171 4 16 1017 4|12 low Small-Large isolated
## 3172 4 16 1017 4|12 low Small-Large isolated
## 3173 4 16 1017 4|12 low Small-Large isolated
## 3174 4 16 1017 4|12 low Small-Large isolated
## 3175 4 16 1017 4|12 low Small-Large isolated
## 3176 4 16 1017 4|12 low Small-Large isolated
## 3177 4 16 1017 4|12 low Small-Large isolated
## 3178 4 16 1017 4|12 low Small-Large isolated
## 3179 4 16 1017 4|12 low Small-Large isolated
## 3180 4 16 1017 4|12 low Small-Large isolated
## 3181 4 16 1017 4|12 low Small-Large isolated
## 3182 4 16 1017 4|12 low Small-Large isolated
## 3183 4 16 1017 4|12 low Small-Large isolated
## 3184 4 16 1017 4|12 low Small-Large isolated
## 3185 4 16 1017 4|12 low Small-Large isolated
## 3186 4 16 1017 4|12 low Small-Large isolated
## 3187 4 16 1017 4|12 low Small-Large isolated
## 3188 4 16 1017 4|12 low Small-Large isolated
## 3189 4 16 1017 4|12 low Small-Large isolated
## 3190 4 16 1017 4|12 low Small-Large isolated
## 3191 4 16 1017 4|12 low Small-Large isolated
## 3192 4 16 1017 4|12 low Small-Large isolated
## 3193 5 20 1017 4|12 low Small-Large isolated
## 3194 5 20 1017 4|12 low Small-Large isolated
## 3195 5 20 1017 4|12 low Small-Large isolated
## 3196 5 20 1017 4|12 low Small-Large isolated
## 3197 5 20 1017 4|12 low Small-Large isolated
## 3198 5 20 1017 4|12 low Small-Large isolated
## 3199 5 20 1017 4|12 low Small-Large isolated
## 3200 5 20 1017 4|12 low Small-Large isolated
## 3201 5 20 1017 4|12 low Small-Large isolated
## 3202 5 20 1017 4|12 low Small-Large isolated
## 3203 5 20 1017 4|12 low Small-Large isolated
## 3204 5 20 1017 4|12 low Small-Large isolated
## 3205 5 20 1017 4|12 low Small-Large isolated
## 3206 5 20 1017 4|12 low Small-Large isolated
## 3207 5 20 1017 4|12 low Small-Large isolated
## 3208 5 20 1017 4|12 low Small-Large isolated
## 3209 5 20 1017 4|12 low Small-Large isolated
## 3210 5 20 1017 4|12 low Small-Large isolated
## 3211 5 20 1017 4|12 low Small-Large isolated
## 3212 5 20 1017 4|12 low Small-Large isolated
## 3213 5 20 1017 4|12 low Small-Large isolated
## 3214 5 20 1017 4|12 low Small-Large isolated
## 3215 5 20 1017 4|12 low Small-Large isolated
## 3216 5 20 1017 4|12 low Small-Large isolated
## 3217 6 24 1017 4|12 low Small-Large isolated
## 3218 6 24 1017 4|12 low Small-Large isolated
## 3219 6 24 1017 4|12 low Small-Large isolated
## 3220 6 24 1017 4|12 low Small-Large isolated
## 3221 6 24 1017 4|12 low Small-Large isolated
## 3222 6 24 1017 4|12 low Small-Large isolated
## 3223 6 24 1017 4|12 low Small-Large isolated
## 3224 6 24 1017 4|12 low Small-Large isolated
## 3225 6 24 1017 4|12 low Small-Large isolated
## mean_shannon mean_richness jaccard_index bray_curtis beta_spatial_turnover
## 1 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 2 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 3 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 4 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 5 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 6 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 7 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 8 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 9 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 10 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 11 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 12 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 13 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 14 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 15 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 16 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 17 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 18 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 19 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 20 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 21 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 22 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 23 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 24 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 25 1.5003642 7.5 0.3797327 0.2343643 0.1428571
## 26 1.5003642 7.5 0.3797327 0.2343643 0.1428571
## 27 1.5003642 7.5 0.3797327 0.2343643 0.1428571
## 28 1.5003642 7.5 0.3797327 0.2343643 0.1428571
## 29 1.5003642 7.5 0.3797327 0.2343643 0.1428571
## 30 1.5003642 7.5 0.3797327 0.2343643 0.1428571
## 31 1.5003642 7.5 0.3797327 0.2343643 0.1428571
## 32 1.5003642 7.5 0.3797327 0.2343643 0.1428571
## 33 1.5003642 7.5 0.3797327 0.2343643 0.1428571
## 34 1.5003642 7.5 0.3797327 0.2343643 0.1428571
## 35 1.5003642 7.5 0.3797327 0.2343643 0.1428571
## 36 1.5003642 7.5 0.3797327 0.2343643 0.1428571
## 37 1.5003642 7.5 0.3797327 0.2343643 0.1428571
## 38 1.5003642 7.5 0.3797327 0.2343643 0.1428571
## 39 1.5003642 7.5 0.3797327 0.2343643 0.1428571
## 40 1.5003642 7.5 0.3797327 0.2343643 0.1428571
## 41 1.5003642 7.5 0.3797327 0.2343643 0.1428571
## 42 1.5003642 7.5 0.3797327 0.2343643 0.1428571
## 43 1.5003642 7.5 0.3797327 0.2343643 0.1428571
## 44 1.5003642 7.5 0.3797327 0.2343643 0.1428571
## 45 1.5003642 7.5 0.3797327 0.2343643 0.1428571
## 46 1.5003642 7.5 0.3797327 0.2343643 0.1428571
## 47 1.5003642 7.5 0.3797327 0.2343643 0.1428571
## 48 1.5003642 7.5 0.3797327 0.2343643 0.1428571
## 49 1.5643953 8.0 0.5027346 0.3357686 0.1250000
## 50 1.5643953 8.0 0.5027346 0.3357686 0.1250000
## 51 1.5643953 8.0 0.5027346 0.3357686 0.1250000
## 52 1.5643953 8.0 0.5027346 0.3357686 0.1250000
## 53 1.5643953 8.0 0.5027346 0.3357686 0.1250000
## 54 1.5643953 8.0 0.5027346 0.3357686 0.1250000
## 55 1.5643953 8.0 0.5027346 0.3357686 0.1250000
## 56 1.5643953 8.0 0.5027346 0.3357686 0.1250000
## 57 1.5643953 8.0 0.5027346 0.3357686 0.1250000
## 58 1.5643953 8.0 0.5027346 0.3357686 0.1250000
## 59 1.5643953 8.0 0.5027346 0.3357686 0.1250000
## 60 1.5643953 8.0 0.5027346 0.3357686 0.1250000
## 61 1.5643953 8.0 0.5027346 0.3357686 0.1250000
## 62 1.5643953 8.0 0.5027346 0.3357686 0.1250000
## 63 1.5643953 8.0 0.5027346 0.3357686 0.1250000
## 64 1.5643953 8.0 0.5027346 0.3357686 0.1250000
## 65 1.5643953 8.0 0.5027346 0.3357686 0.1250000
## 66 1.5643953 8.0 0.5027346 0.3357686 0.1250000
## 67 1.5643953 8.0 0.5027346 0.3357686 0.1250000
## 68 1.5643953 8.0 0.5027346 0.3357686 0.1250000
## 69 1.5643953 8.0 0.5027346 0.3357686 0.1250000
## 70 1.5643953 8.0 0.5027346 0.3357686 0.1250000
## 71 1.5643953 8.0 0.5027346 0.3357686 0.1250000
## 72 1.5643953 8.0 0.5027346 0.3357686 0.1250000
## 73 1.6095690 6.0 0.8561835 0.7485323 0.0000000
## 74 1.6095690 6.0 0.8561835 0.7485323 0.0000000
## 75 1.6095690 6.0 0.8561835 0.7485323 0.0000000
## 76 1.6095690 6.0 0.8561835 0.7485323 0.0000000
## 77 1.6095690 6.0 0.8561835 0.7485323 0.0000000
## 78 1.6095690 6.0 0.8561835 0.7485323 0.0000000
## 79 1.6095690 6.0 0.8561835 0.7485323 0.0000000
## 80 1.6095690 6.0 0.8561835 0.7485323 0.0000000
## 81 1.6095690 6.0 0.8561835 0.7485323 0.0000000
## 82 1.6095690 6.0 0.8561835 0.7485323 0.0000000
## 83 1.6095690 6.0 0.8561835 0.7485323 0.0000000
## 84 1.6095690 6.0 0.8561835 0.7485323 0.0000000
## 85 1.6095690 6.0 0.8561835 0.7485323 0.0000000
## 86 1.6095690 6.0 0.8561835 0.7485323 0.0000000
## 87 1.6095690 6.0 0.8561835 0.7485323 0.0000000
## 88 1.6095690 6.0 0.8561835 0.7485323 0.0000000
## 89 1.6095690 6.0 0.8561835 0.7485323 0.0000000
## 90 1.6095690 6.0 0.8561835 0.7485323 0.0000000
## 91 1.6095690 6.0 0.8561835 0.7485323 0.0000000
## 92 1.6095690 6.0 0.8561835 0.7485323 0.0000000
## 93 1.6095690 6.0 0.8561835 0.7485323 0.0000000
## 94 1.6095690 6.0 0.8561835 0.7485323 0.0000000
## 95 1.6095690 6.0 0.8561835 0.7485323 0.0000000
## 96 1.6095690 6.0 0.8561835 0.7485323 0.0000000
## 97 1.8022460 7.5 0.4038401 0.2530073 0.1428571
## 98 1.8022460 7.5 0.4038401 0.2530073 0.1428571
## 99 1.8022460 7.5 0.4038401 0.2530073 0.1428571
## 100 1.8022460 7.5 0.4038401 0.2530073 0.1428571
## 101 1.8022460 7.5 0.4038401 0.2530073 0.1428571
## 102 1.8022460 7.5 0.4038401 0.2530073 0.1428571
## 103 1.8022460 7.5 0.4038401 0.2530073 0.1428571
## 104 1.8022460 7.5 0.4038401 0.2530073 0.1428571
## 105 1.8022460 7.5 0.4038401 0.2530073 0.1428571
## 106 1.8022460 7.5 0.4038401 0.2530073 0.1428571
## 107 1.8022460 7.5 0.4038401 0.2530073 0.1428571
## 108 1.8022460 7.5 0.4038401 0.2530073 0.1428571
## 109 1.8022460 7.5 0.4038401 0.2530073 0.1428571
## 110 1.8022460 7.5 0.4038401 0.2530073 0.1428571
## 111 1.8022460 7.5 0.4038401 0.2530073 0.1428571
## 112 1.8022460 7.5 0.4038401 0.2530073 0.1428571
## 113 1.8022460 7.5 0.4038401 0.2530073 0.1428571
## 114 1.8022460 7.5 0.4038401 0.2530073 0.1428571
## 115 1.8022460 7.5 0.4038401 0.2530073 0.1428571
## 116 1.8022460 7.5 0.4038401 0.2530073 0.1428571
## 117 1.8022460 7.5 0.4038401 0.2530073 0.1428571
## 118 1.8022460 7.5 0.4038401 0.2530073 0.1428571
## 119 1.8022460 7.5 0.4038401 0.2530073 0.1428571
## 120 1.8022460 7.5 0.4038401 0.2530073 0.1428571
## 121 0.9046531 4.0 0.8022244 0.6697618 0.0000000
## 122 0.9046531 4.0 0.8022244 0.6697618 0.0000000
## 123 0.9046531 4.0 0.8022244 0.6697618 0.0000000
## 124 0.9046531 4.0 0.8022244 0.6697618 0.0000000
## 125 0.9046531 4.0 0.8022244 0.6697618 0.0000000
## 126 0.9046531 4.0 0.8022244 0.6697618 0.0000000
## 127 0.9046531 4.0 0.8022244 0.6697618 0.0000000
## 128 0.9046531 4.0 0.8022244 0.6697618 0.0000000
## 129 0.9046531 4.0 0.8022244 0.6697618 0.0000000
## 130 0.9046531 4.0 0.8022244 0.6697618 0.0000000
## 131 0.9046531 4.0 0.8022244 0.6697618 0.0000000
## 132 0.9046531 4.0 0.8022244 0.6697618 0.0000000
## 133 0.9046531 4.0 0.8022244 0.6697618 0.0000000
## 134 0.9046531 4.0 0.8022244 0.6697618 0.0000000
## 135 0.9046531 4.0 0.8022244 0.6697618 0.0000000
## 136 0.9046531 4.0 0.8022244 0.6697618 0.0000000
## 137 0.9046531 4.0 0.8022244 0.6697618 0.0000000
## 138 0.9046531 4.0 0.8022244 0.6697618 0.0000000
## 139 0.9046531 4.0 0.8022244 0.6697618 0.0000000
## 140 0.9046531 4.0 0.8022244 0.6697618 0.0000000
## 141 0.9046531 4.0 0.8022244 0.6697618 0.0000000
## 142 0.9046531 4.0 0.8022244 0.6697618 0.0000000
## 143 0.9046531 4.0 0.8022244 0.6697618 0.0000000
## 144 0.9046531 4.0 0.8022244 0.6697618 0.0000000
## 145 1.1722772 6.0 0.8009542 0.6679930 0.4000000
## 146 1.1722772 6.0 0.8009542 0.6679930 0.4000000
## 147 1.1722772 6.0 0.8009542 0.6679930 0.4000000
## 148 1.1722772 6.0 0.8009542 0.6679930 0.4000000
## 149 1.1722772 6.0 0.8009542 0.6679930 0.4000000
## 150 1.1722772 6.0 0.8009542 0.6679930 0.4000000
## 151 1.1722772 6.0 0.8009542 0.6679930 0.4000000
## 152 1.1722772 6.0 0.8009542 0.6679930 0.4000000
## 153 1.1722772 6.0 0.8009542 0.6679930 0.4000000
## 154 1.1722772 6.0 0.8009542 0.6679930 0.4000000
## 155 1.1722772 6.0 0.8009542 0.6679930 0.4000000
## 156 1.1722772 6.0 0.8009542 0.6679930 0.4000000
## 157 1.1722772 6.0 0.8009542 0.6679930 0.4000000
## 158 1.1722772 6.0 0.8009542 0.6679930 0.4000000
## 159 1.1722772 6.0 0.8009542 0.6679930 0.4000000
## 160 1.1722772 6.0 0.8009542 0.6679930 0.4000000
## 161 1.1722772 6.0 0.8009542 0.6679930 0.4000000
## 162 1.1722772 6.0 0.8009542 0.6679930 0.4000000
## 163 1.1722772 6.0 0.8009542 0.6679930 0.4000000
## 164 1.1722772 6.0 0.8009542 0.6679930 0.4000000
## 165 1.1722772 6.0 0.8009542 0.6679930 0.4000000
## 166 1.1722772 6.0 0.8009542 0.6679930 0.4000000
## 167 1.1722772 6.0 0.8009542 0.6679930 0.4000000
## 168 1.1722772 6.0 0.8009542 0.6679930 0.4000000
## 169 0.9415572 5.5 0.9159832 0.8449899 0.0000000
## 170 0.9415572 5.5 0.9159832 0.8449899 0.0000000
## 171 0.9415572 5.5 0.9159832 0.8449899 0.0000000
## 172 0.9415572 5.5 0.9159832 0.8449899 0.0000000
## 173 0.9415572 5.5 0.9159832 0.8449899 0.0000000
## 174 0.9415572 5.5 0.9159832 0.8449899 0.0000000
## 175 0.9415572 5.5 0.9159832 0.8449899 0.0000000
## 176 0.9415572 5.5 0.9159832 0.8449899 0.0000000
## 177 0.9415572 5.5 0.9159832 0.8449899 0.0000000
## 178 0.9415572 5.5 0.9159832 0.8449899 0.0000000
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## 822 1.6208829 7.5 0.5405548 0.3703838 0.1428571
## 823 1.6208829 7.5 0.5405548 0.3703838 0.1428571
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## 828 1.6208829 7.5 0.5405548 0.3703838 0.1428571
## 829 1.6208829 7.5 0.5405548 0.3703838 0.1428571
## 830 1.6208829 7.5 0.5405548 0.3703838 0.1428571
## 831 1.6208829 7.5 0.5405548 0.3703838 0.1428571
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## 834 1.6208829 7.5 0.5405548 0.3703838 0.1428571
## 835 1.6208829 7.5 0.5405548 0.3703838 0.1428571
## 836 1.6208829 7.5 0.5405548 0.3703838 0.1428571
## 837 1.6208829 7.5 0.5405548 0.3703838 0.1428571
## 838 1.6208829 7.5 0.5405548 0.3703838 0.1428571
## 839 1.6208829 7.5 0.5405548 0.3703838 0.1428571
## 840 1.6208829 7.5 0.5405548 0.3703838 0.1428571
## 841 1.5861251 6.0 0.8562784 0.7486772 0.0000000
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## 843 1.5861251 6.0 0.8562784 0.7486772 0.0000000
## 844 1.5861251 6.0 0.8562784 0.7486772 0.0000000
## 845 1.5861251 6.0 0.8562784 0.7486772 0.0000000
## 846 1.5861251 6.0 0.8562784 0.7486772 0.0000000
## 847 1.5861251 6.0 0.8562784 0.7486772 0.0000000
## 848 1.5861251 6.0 0.8562784 0.7486772 0.0000000
## 849 1.5861251 6.0 0.8562784 0.7486772 0.0000000
## 850 1.5861251 6.0 0.8562784 0.7486772 0.0000000
## 851 1.5861251 6.0 0.8562784 0.7486772 0.0000000
## 852 1.5861251 6.0 0.8562784 0.7486772 0.0000000
## 853 1.5861251 6.0 0.8562784 0.7486772 0.0000000
## 854 1.5861251 6.0 0.8562784 0.7486772 0.0000000
## 855 1.5861251 6.0 0.8562784 0.7486772 0.0000000
## 856 1.5861251 6.0 0.8562784 0.7486772 0.0000000
## 857 1.5861251 6.0 0.8562784 0.7486772 0.0000000
## 858 1.5861251 6.0 0.8562784 0.7486772 0.0000000
## 859 1.5861251 6.0 0.8562784 0.7486772 0.0000000
## 860 1.5861251 6.0 0.8562784 0.7486772 0.0000000
## 861 1.5861251 6.0 0.8562784 0.7486772 0.0000000
## 862 1.5861251 6.0 0.8562784 0.7486772 0.0000000
## 863 1.5861251 6.0 0.8562784 0.7486772 0.0000000
## 864 1.5861251 6.0 0.8562784 0.7486772 0.0000000
## 865 1.6705149 7.5 0.5076640 0.3401808 0.1428571
## 866 1.6705149 7.5 0.5076640 0.3401808 0.1428571
## 867 1.6705149 7.5 0.5076640 0.3401808 0.1428571
## 868 1.6705149 7.5 0.5076640 0.3401808 0.1428571
## 869 1.6705149 7.5 0.5076640 0.3401808 0.1428571
## 870 1.6705149 7.5 0.5076640 0.3401808 0.1428571
## 871 1.6705149 7.5 0.5076640 0.3401808 0.1428571
## 872 1.6705149 7.5 0.5076640 0.3401808 0.1428571
## 873 1.6705149 7.5 0.5076640 0.3401808 0.1428571
## 874 1.6705149 7.5 0.5076640 0.3401808 0.1428571
## 875 1.6705149 7.5 0.5076640 0.3401808 0.1428571
## 876 1.6705149 7.5 0.5076640 0.3401808 0.1428571
## 877 1.6705149 7.5 0.5076640 0.3401808 0.1428571
## 878 1.6705149 7.5 0.5076640 0.3401808 0.1428571
## 879 1.6705149 7.5 0.5076640 0.3401808 0.1428571
## 880 1.6705149 7.5 0.5076640 0.3401808 0.1428571
## 881 1.6705149 7.5 0.5076640 0.3401808 0.1428571
## 882 1.6705149 7.5 0.5076640 0.3401808 0.1428571
## 883 1.6705149 7.5 0.5076640 0.3401808 0.1428571
## 884 1.6705149 7.5 0.5076640 0.3401808 0.1428571
## 885 1.6705149 7.5 0.5076640 0.3401808 0.1428571
## 886 1.6705149 7.5 0.5076640 0.3401808 0.1428571
## 887 1.6705149 7.5 0.5076640 0.3401808 0.1428571
## 888 1.6705149 7.5 0.5076640 0.3401808 0.1428571
## 889 1.1807457 5.0 0.8075314 0.6771930 0.0000000
## 890 1.1807457 5.0 0.8075314 0.6771930 0.0000000
## 891 1.1807457 5.0 0.8075314 0.6771930 0.0000000
## 892 1.1807457 5.0 0.8075314 0.6771930 0.0000000
## 893 1.1807457 5.0 0.8075314 0.6771930 0.0000000
## 894 1.1807457 5.0 0.8075314 0.6771930 0.0000000
## 895 1.1807457 5.0 0.8075314 0.6771930 0.0000000
## 896 1.1807457 5.0 0.8075314 0.6771930 0.0000000
## 897 1.1807457 5.0 0.8075314 0.6771930 0.0000000
## 898 1.1807457 5.0 0.8075314 0.6771930 0.0000000
## 899 1.1807457 5.0 0.8075314 0.6771930 0.0000000
## 900 1.1807457 5.0 0.8075314 0.6771930 0.0000000
## 901 1.1807457 5.0 0.8075314 0.6771930 0.0000000
## 902 1.1807457 5.0 0.8075314 0.6771930 0.0000000
## 903 1.1807457 5.0 0.8075314 0.6771930 0.0000000
## 904 1.1807457 5.0 0.8075314 0.6771930 0.0000000
## 905 1.1807457 5.0 0.8075314 0.6771930 0.0000000
## 906 1.1807457 5.0 0.8075314 0.6771930 0.0000000
## 907 1.1807457 5.0 0.8075314 0.6771930 0.0000000
## 908 1.1807457 5.0 0.8075314 0.6771930 0.0000000
## 909 1.1807457 5.0 0.8075314 0.6771930 0.0000000
## 910 1.1807457 5.0 0.8075314 0.6771930 0.0000000
## 911 1.1807457 5.0 0.8075314 0.6771930 0.0000000
## 912 1.1807457 5.0 0.8075314 0.6771930 0.0000000
## 913 1.5071598 6.5 0.8093303 0.6797269 0.2000000
## 914 1.5071598 6.5 0.8093303 0.6797269 0.2000000
## 915 1.5071598 6.5 0.8093303 0.6797269 0.2000000
## 916 1.5071598 6.5 0.8093303 0.6797269 0.2000000
## 917 1.5071598 6.5 0.8093303 0.6797269 0.2000000
## 918 1.5071598 6.5 0.8093303 0.6797269 0.2000000
## 919 1.5071598 6.5 0.8093303 0.6797269 0.2000000
## 920 1.5071598 6.5 0.8093303 0.6797269 0.2000000
## 921 1.5071598 6.5 0.8093303 0.6797269 0.2000000
## 922 1.5071598 6.5 0.8093303 0.6797269 0.2000000
## 923 1.5071598 6.5 0.8093303 0.6797269 0.2000000
## 924 1.5071598 6.5 0.8093303 0.6797269 0.2000000
## 925 1.5071598 6.5 0.8093303 0.6797269 0.2000000
## 926 1.5071598 6.5 0.8093303 0.6797269 0.2000000
## 927 1.5071598 6.5 0.8093303 0.6797269 0.2000000
## 928 1.5071598 6.5 0.8093303 0.6797269 0.2000000
## 929 1.5071598 6.5 0.8093303 0.6797269 0.2000000
## 930 1.5071598 6.5 0.8093303 0.6797269 0.2000000
## 931 1.5071598 6.5 0.8093303 0.6797269 0.2000000
## 932 1.5071598 6.5 0.8093303 0.6797269 0.2000000
## 933 1.5071598 6.5 0.8093303 0.6797269 0.2000000
## 934 1.5071598 6.5 0.8093303 0.6797269 0.2000000
## 935 1.5071598 6.5 0.8093303 0.6797269 0.2000000
## 936 1.5071598 6.5 0.8093303 0.6797269 0.2000000
## 937 0.9391687 5.0 0.9203269 0.8524125 0.0000000
## 938 0.9391687 5.0 0.9203269 0.8524125 0.0000000
## 939 0.9391687 5.0 0.9203269 0.8524125 0.0000000
## 940 0.9391687 5.0 0.9203269 0.8524125 0.0000000
## 941 0.9391687 5.0 0.9203269 0.8524125 0.0000000
## 942 0.9391687 5.0 0.9203269 0.8524125 0.0000000
## 943 0.9391687 5.0 0.9203269 0.8524125 0.0000000
## 944 0.9391687 5.0 0.9203269 0.8524125 0.0000000
## 945 0.9391687 5.0 0.9203269 0.8524125 0.0000000
## 946 0.9391687 5.0 0.9203269 0.8524125 0.0000000
## 947 0.9391687 5.0 0.9203269 0.8524125 0.0000000
## 948 0.9391687 5.0 0.9203269 0.8524125 0.0000000
## 949 0.9391687 5.0 0.9203269 0.8524125 0.0000000
## 950 0.9391687 5.0 0.9203269 0.8524125 0.0000000
## 951 0.9391687 5.0 0.9203269 0.8524125 0.0000000
## 952 0.9391687 5.0 0.9203269 0.8524125 0.0000000
## 953 0.9391687 5.0 0.9203269 0.8524125 0.0000000
## 954 0.9391687 5.0 0.9203269 0.8524125 0.0000000
## 955 0.9391687 5.0 0.9203269 0.8524125 0.0000000
## 956 0.9391687 5.0 0.9203269 0.8524125 0.0000000
## 957 0.9391687 5.0 0.9203269 0.8524125 0.0000000
## 958 0.9391687 5.0 0.9203269 0.8524125 0.0000000
## 959 0.9391687 5.0 0.9203269 0.8524125 0.0000000
## 960 0.9391687 5.0 0.9203269 0.8524125 0.0000000
## 961 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 962 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 963 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 964 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 965 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 966 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 967 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 968 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 969 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 970 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 971 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 972 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 973 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 974 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 975 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 976 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 977 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 978 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 979 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 980 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 981 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 982 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 983 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 984 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 985 1.5540186 8.0 0.4775502 0.3136722 0.2500000
## 986 1.5540186 8.0 0.4775502 0.3136722 0.2500000
## 987 1.5540186 8.0 0.4775502 0.3136722 0.2500000
## 988 1.5540186 8.0 0.4775502 0.3136722 0.2500000
## 989 1.5540186 8.0 0.4775502 0.3136722 0.2500000
## 990 1.5540186 8.0 0.4775502 0.3136722 0.2500000
## 991 1.5540186 8.0 0.4775502 0.3136722 0.2500000
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## 993 1.5540186 8.0 0.4775502 0.3136722 0.2500000
## 994 1.5540186 8.0 0.4775502 0.3136722 0.2500000
## 995 1.5540186 8.0 0.4775502 0.3136722 0.2500000
## 996 1.5540186 8.0 0.4775502 0.3136722 0.2500000
## 997 1.5540186 8.0 0.4775502 0.3136722 0.2500000
## 998 1.5540186 8.0 0.4775502 0.3136722 0.2500000
## 999 1.5540186 8.0 0.4775502 0.3136722 0.2500000
## 1000 1.5540186 8.0 0.4775502 0.3136722 0.2500000
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## 1002 1.5540186 8.0 0.4775502 0.3136722 0.2500000
## 1003 1.5540186 8.0 0.4775502 0.3136722 0.2500000
## 1004 1.5540186 8.0 0.4775502 0.3136722 0.2500000
## 1005 1.5540186 8.0 0.4775502 0.3136722 0.2500000
## 1006 1.5540186 8.0 0.4775502 0.3136722 0.2500000
## 1007 1.5540186 8.0 0.4775502 0.3136722 0.2500000
## 1008 1.5540186 8.0 0.4775502 0.3136722 0.2500000
## 1009 1.5213817 8.5 0.6802785 0.5154712 0.1250000
## 1010 1.5213817 8.5 0.6802785 0.5154712 0.1250000
## 1011 1.5213817 8.5 0.6802785 0.5154712 0.1250000
## 1012 1.5213817 8.5 0.6802785 0.5154712 0.1250000
## 1013 1.5213817 8.5 0.6802785 0.5154712 0.1250000
## 1014 1.5213817 8.5 0.6802785 0.5154712 0.1250000
## 1015 1.5213817 8.5 0.6802785 0.5154712 0.1250000
## 1016 1.5213817 8.5 0.6802785 0.5154712 0.1250000
## 1017 1.5213817 8.5 0.6802785 0.5154712 0.1250000
## 1018 1.5213817 8.5 0.6802785 0.5154712 0.1250000
## 1019 1.5213817 8.5 0.6802785 0.5154712 0.1250000
## 1020 1.5213817 8.5 0.6802785 0.5154712 0.1250000
## 1021 1.5213817 8.5 0.6802785 0.5154712 0.1250000
## 1022 1.5213817 8.5 0.6802785 0.5154712 0.1250000
## 1023 1.5213817 8.5 0.6802785 0.5154712 0.1250000
## 1024 1.5213817 8.5 0.6802785 0.5154712 0.1250000
## 1025 1.5213817 8.5 0.6802785 0.5154712 0.1250000
## 1026 1.5213817 8.5 0.6802785 0.5154712 0.1250000
## 1027 1.5213817 8.5 0.6802785 0.5154712 0.1250000
## 1028 1.5213817 8.5 0.6802785 0.5154712 0.1250000
## 1029 1.5213817 8.5 0.6802785 0.5154712 0.1250000
## 1030 1.5213817 8.5 0.6802785 0.5154712 0.1250000
## 1031 1.5213817 8.5 0.6802785 0.5154712 0.1250000
## 1032 1.5213817 8.5 0.6802785 0.5154712 0.1250000
## 1033 1.7632593 7.0 0.4519279 0.2919295 0.0000000
## 1034 1.7632593 7.0 0.4519279 0.2919295 0.0000000
## 1035 1.7632593 7.0 0.4519279 0.2919295 0.0000000
## 1036 1.7632593 7.0 0.4519279 0.2919295 0.0000000
## 1037 1.7632593 7.0 0.4519279 0.2919295 0.0000000
## 1038 1.7632593 7.0 0.4519279 0.2919295 0.0000000
## 1039 1.7632593 7.0 0.4519279 0.2919295 0.0000000
## 1040 1.7632593 7.0 0.4519279 0.2919295 0.0000000
## 1041 1.7632593 7.0 0.4519279 0.2919295 0.0000000
## 1042 1.7632593 7.0 0.4519279 0.2919295 0.0000000
## 1043 1.7632593 7.0 0.4519279 0.2919295 0.0000000
## 1044 1.7632593 7.0 0.4519279 0.2919295 0.0000000
## 1045 1.7632593 7.0 0.4519279 0.2919295 0.0000000
## 1046 1.7632593 7.0 0.4519279 0.2919295 0.0000000
## 1047 1.7632593 7.0 0.4519279 0.2919295 0.0000000
## 1048 1.7632593 7.0 0.4519279 0.2919295 0.0000000
## 1049 1.7632593 7.0 0.4519279 0.2919295 0.0000000
## 1050 1.7632593 7.0 0.4519279 0.2919295 0.0000000
## 1051 1.7632593 7.0 0.4519279 0.2919295 0.0000000
## 1052 1.7632593 7.0 0.4519279 0.2919295 0.0000000
## 1053 1.7632593 7.0 0.4519279 0.2919295 0.0000000
## 1054 1.7632593 7.0 0.4519279 0.2919295 0.0000000
## 1055 1.7632593 7.0 0.4519279 0.2919295 0.0000000
## 1056 1.7632593 7.0 0.4519279 0.2919295 0.0000000
## 1057 1.3837136 5.5 0.6129142 0.4418719 0.0000000
## 1058 1.3837136 5.5 0.6129142 0.4418719 0.0000000
## 1059 1.3837136 5.5 0.6129142 0.4418719 0.0000000
## 1060 1.3837136 5.5 0.6129142 0.4418719 0.0000000
## 1061 1.3837136 5.5 0.6129142 0.4418719 0.0000000
## 1062 1.3837136 5.5 0.6129142 0.4418719 0.0000000
## 1063 1.3837136 5.5 0.6129142 0.4418719 0.0000000
## 1064 1.3837136 5.5 0.6129142 0.4418719 0.0000000
## 1065 1.3837136 5.5 0.6129142 0.4418719 0.0000000
## 1066 1.3837136 5.5 0.6129142 0.4418719 0.0000000
## 1067 1.3837136 5.5 0.6129142 0.4418719 0.0000000
## 1068 1.3837136 5.5 0.6129142 0.4418719 0.0000000
## 1069 1.3837136 5.5 0.6129142 0.4418719 0.0000000
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## 1071 1.3837136 5.5 0.6129142 0.4418719 0.0000000
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## 1073 1.3837136 5.5 0.6129142 0.4418719 0.0000000
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## 1075 1.3837136 5.5 0.6129142 0.4418719 0.0000000
## 1076 1.3837136 5.5 0.6129142 0.4418719 0.0000000
## 1077 1.3837136 5.5 0.6129142 0.4418719 0.0000000
## 1078 1.3837136 5.5 0.6129142 0.4418719 0.0000000
## 1079 1.3837136 5.5 0.6129142 0.4418719 0.0000000
## 1080 1.3837136 5.5 0.6129142 0.4418719 0.0000000
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## 1082 1.1906786 4.5 0.9314463 0.8716887 0.3333333
## 1083 1.1906786 4.5 0.9314463 0.8716887 0.3333333
## 1084 1.1906786 4.5 0.9314463 0.8716887 0.3333333
## 1085 1.1906786 4.5 0.9314463 0.8716887 0.3333333
## 1086 1.1906786 4.5 0.9314463 0.8716887 0.3333333
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## 1089 1.1906786 4.5 0.9314463 0.8716887 0.3333333
## 1090 1.1906786 4.5 0.9314463 0.8716887 0.3333333
## 1091 1.1906786 4.5 0.9314463 0.8716887 0.3333333
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## 1098 1.1906786 4.5 0.9314463 0.8716887 0.3333333
## 1099 1.1906786 4.5 0.9314463 0.8716887 0.3333333
## 1100 1.1906786 4.5 0.9314463 0.8716887 0.3333333
## 1101 1.1906786 4.5 0.9314463 0.8716887 0.3333333
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## 1103 1.1906786 4.5 0.9314463 0.8716887 0.3333333
## 1104 1.1906786 4.5 0.9314463 0.8716887 0.3333333
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## 1125 0.5580146 4.0 0.8940736 0.8084387 0.0000000
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## 1127 0.5580146 4.0 0.8940736 0.8084387 0.0000000
## 1128 0.5580146 4.0 0.8940736 0.8084387 0.0000000
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## 1130 0.6573424 5.0 0.9461440 0.8977925 0.0000000
## 1131 0.6573424 5.0 0.9461440 0.8977925 0.0000000
## 1132 0.6573424 5.0 0.9461440 0.8977925 0.0000000
## 1133 0.6573424 5.0 0.9461440 0.8977925 0.0000000
## 1134 0.6573424 5.0 0.9461440 0.8977925 0.0000000
## 1135 0.6573424 5.0 0.9461440 0.8977925 0.0000000
## 1136 0.6573424 5.0 0.9461440 0.8977925 0.0000000
## 1137 0.6573424 5.0 0.9461440 0.8977925 0.0000000
## 1138 0.6573424 5.0 0.9461440 0.8977925 0.0000000
## 1139 0.6573424 5.0 0.9461440 0.8977925 0.0000000
## 1140 0.6573424 5.0 0.9461440 0.8977925 0.0000000
## 1141 0.6573424 5.0 0.9461440 0.8977925 0.0000000
## 1142 0.6573424 5.0 0.9461440 0.8977925 0.0000000
## 1143 0.6573424 5.0 0.9461440 0.8977925 0.0000000
## 1144 0.6573424 5.0 0.9461440 0.8977925 0.0000000
## 1145 0.6573424 5.0 0.9461440 0.8977925 0.0000000
## 1146 0.6573424 5.0 0.9461440 0.8977925 0.0000000
## 1147 0.6573424 5.0 0.9461440 0.8977925 0.0000000
## 1148 0.6573424 5.0 0.9461440 0.8977925 0.0000000
## 1149 0.6573424 5.0 0.9461440 0.8977925 0.0000000
## 1150 0.6573424 5.0 0.9461440 0.8977925 0.0000000
## 1151 0.6573424 5.0 0.9461440 0.8977925 0.0000000
## 1152 0.6573424 5.0 0.9461440 0.8977925 0.0000000
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## 1172 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 1173 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 1174 2.0784670 11.0 0.0000000 0.0000000 0.0000000
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## 1178 1.7881218 8.5 0.4851643 0.3202752 0.1250000
## 1179 1.7881218 8.5 0.4851643 0.3202752 0.1250000
## 1180 1.7881218 8.5 0.4851643 0.3202752 0.1250000
## 1181 1.7881218 8.5 0.4851643 0.3202752 0.1250000
## 1182 1.7881218 8.5 0.4851643 0.3202752 0.1250000
## 1183 1.7881218 8.5 0.4851643 0.3202752 0.1250000
## 1184 1.7881218 8.5 0.4851643 0.3202752 0.1250000
## 1185 1.7881218 8.5 0.4851643 0.3202752 0.1250000
## 1186 1.7881218 8.5 0.4851643 0.3202752 0.1250000
## 1187 1.7881218 8.5 0.4851643 0.3202752 0.1250000
## 1188 1.7881218 8.5 0.4851643 0.3202752 0.1250000
## 1189 1.7881218 8.5 0.4851643 0.3202752 0.1250000
## 1190 1.7881218 8.5 0.4851643 0.3202752 0.1250000
## 1191 1.7881218 8.5 0.4851643 0.3202752 0.1250000
## 1192 1.7881218 8.5 0.4851643 0.3202752 0.1250000
## 1193 1.7881218 8.5 0.4851643 0.3202752 0.1250000
## 1194 1.7881218 8.5 0.4851643 0.3202752 0.1250000
## 1195 1.7881218 8.5 0.4851643 0.3202752 0.1250000
## 1196 1.7881218 8.5 0.4851643 0.3202752 0.1250000
## 1197 1.7881218 8.5 0.4851643 0.3202752 0.1250000
## 1198 1.7881218 8.5 0.4851643 0.3202752 0.1250000
## 1199 1.7881218 8.5 0.4851643 0.3202752 0.1250000
## 1200 1.7881218 8.5 0.4851643 0.3202752 0.1250000
## 1201 1.4483385 7.0 0.6127187 0.4416686 0.0000000
## 1202 1.4483385 7.0 0.6127187 0.4416686 0.0000000
## 1203 1.4483385 7.0 0.6127187 0.4416686 0.0000000
## 1204 1.4483385 7.0 0.6127187 0.4416686 0.0000000
## 1205 1.4483385 7.0 0.6127187 0.4416686 0.0000000
## 1206 1.4483385 7.0 0.6127187 0.4416686 0.0000000
## 1207 1.4483385 7.0 0.6127187 0.4416686 0.0000000
## 1208 1.4483385 7.0 0.6127187 0.4416686 0.0000000
## 1209 1.4483385 7.0 0.6127187 0.4416686 0.0000000
## 1210 1.4483385 7.0 0.6127187 0.4416686 0.0000000
## 1211 1.4483385 7.0 0.6127187 0.4416686 0.0000000
## 1212 1.4483385 7.0 0.6127187 0.4416686 0.0000000
## 1213 1.4483385 7.0 0.6127187 0.4416686 0.0000000
## 1214 1.4483385 7.0 0.6127187 0.4416686 0.0000000
## 1215 1.4483385 7.0 0.6127187 0.4416686 0.0000000
## 1216 1.4483385 7.0 0.6127187 0.4416686 0.0000000
## 1217 1.4483385 7.0 0.6127187 0.4416686 0.0000000
## 1218 1.4483385 7.0 0.6127187 0.4416686 0.0000000
## 1219 1.4483385 7.0 0.6127187 0.4416686 0.0000000
## 1220 1.4483385 7.0 0.6127187 0.4416686 0.0000000
## 1221 1.4483385 7.0 0.6127187 0.4416686 0.0000000
## 1222 1.4483385 7.0 0.6127187 0.4416686 0.0000000
## 1223 1.4483385 7.0 0.6127187 0.4416686 0.0000000
## 1224 1.4483385 7.0 0.6127187 0.4416686 0.0000000
## 1225 1.6825243 7.0 0.5191233 0.3505514 0.1666667
## 1226 1.6825243 7.0 0.5191233 0.3505514 0.1666667
## 1227 1.6825243 7.0 0.5191233 0.3505514 0.1666667
## 1228 1.6825243 7.0 0.5191233 0.3505514 0.1666667
## 1229 1.6825243 7.0 0.5191233 0.3505514 0.1666667
## 1230 1.6825243 7.0 0.5191233 0.3505514 0.1666667
## 1231 1.6825243 7.0 0.5191233 0.3505514 0.1666667
## 1232 1.6825243 7.0 0.5191233 0.3505514 0.1666667
## 1233 1.6825243 7.0 0.5191233 0.3505514 0.1666667
## 1234 1.6825243 7.0 0.5191233 0.3505514 0.1666667
## 1235 1.6825243 7.0 0.5191233 0.3505514 0.1666667
## 1236 1.6825243 7.0 0.5191233 0.3505514 0.1666667
## 1237 1.6825243 7.0 0.5191233 0.3505514 0.1666667
## 1238 1.6825243 7.0 0.5191233 0.3505514 0.1666667
## 1239 1.6825243 7.0 0.5191233 0.3505514 0.1666667
## 1240 1.6825243 7.0 0.5191233 0.3505514 0.1666667
## 1241 1.6825243 7.0 0.5191233 0.3505514 0.1666667
## 1242 1.6825243 7.0 0.5191233 0.3505514 0.1666667
## 1243 1.6825243 7.0 0.5191233 0.3505514 0.1666667
## 1244 1.6825243 7.0 0.5191233 0.3505514 0.1666667
## 1245 1.6825243 7.0 0.5191233 0.3505514 0.1666667
## 1246 1.6825243 7.0 0.5191233 0.3505514 0.1666667
## 1247 1.6825243 7.0 0.5191233 0.3505514 0.1666667
## 1248 1.6825243 7.0 0.5191233 0.3505514 0.1666667
## 1249 1.1569293 4.0 0.7495992 0.5994872 0.3333333
## 1250 1.1569293 4.0 0.7495992 0.5994872 0.3333333
## 1251 1.1569293 4.0 0.7495992 0.5994872 0.3333333
## 1252 1.1569293 4.0 0.7495992 0.5994872 0.3333333
## 1253 1.1569293 4.0 0.7495992 0.5994872 0.3333333
## 1254 1.1569293 4.0 0.7495992 0.5994872 0.3333333
## 1255 1.1569293 4.0 0.7495992 0.5994872 0.3333333
## 1256 1.1569293 4.0 0.7495992 0.5994872 0.3333333
## 1257 1.1569293 4.0 0.7495992 0.5994872 0.3333333
## 1258 1.1569293 4.0 0.7495992 0.5994872 0.3333333
## 1259 1.1569293 4.0 0.7495992 0.5994872 0.3333333
## 1260 1.1569293 4.0 0.7495992 0.5994872 0.3333333
## 1261 1.1569293 4.0 0.7495992 0.5994872 0.3333333
## 1262 1.1569293 4.0 0.7495992 0.5994872 0.3333333
## 1263 1.1569293 4.0 0.7495992 0.5994872 0.3333333
## 1264 1.1569293 4.0 0.7495992 0.5994872 0.3333333
## 1265 1.1569293 4.0 0.7495992 0.5994872 0.3333333
## 1266 1.1569293 4.0 0.7495992 0.5994872 0.3333333
## 1267 1.1569293 4.0 0.7495992 0.5994872 0.3333333
## 1268 1.1569293 4.0 0.7495992 0.5994872 0.3333333
## 1269 1.1569293 4.0 0.7495992 0.5994872 0.3333333
## 1270 1.1569293 4.0 0.7495992 0.5994872 0.3333333
## 1271 1.1569293 4.0 0.7495992 0.5994872 0.3333333
## 1272 1.1569293 4.0 0.7495992 0.5994872 0.3333333
## 1273 1.4007946 5.5 0.8509009 0.7404939 0.0000000
## 1274 1.4007946 5.5 0.8509009 0.7404939 0.0000000
## 1275 1.4007946 5.5 0.8509009 0.7404939 0.0000000
## 1276 1.4007946 5.5 0.8509009 0.7404939 0.0000000
## 1277 1.4007946 5.5 0.8509009 0.7404939 0.0000000
## 1278 1.4007946 5.5 0.8509009 0.7404939 0.0000000
## 1279 1.4007946 5.5 0.8509009 0.7404939 0.0000000
## 1280 1.4007946 5.5 0.8509009 0.7404939 0.0000000
## 1281 1.4007946 5.5 0.8509009 0.7404939 0.0000000
## 1282 1.4007946 5.5 0.8509009 0.7404939 0.0000000
## 1283 1.4007946 5.5 0.8509009 0.7404939 0.0000000
## 1284 1.4007946 5.5 0.8509009 0.7404939 0.0000000
## 1285 1.4007946 5.5 0.8509009 0.7404939 0.0000000
## 1286 1.4007946 5.5 0.8509009 0.7404939 0.0000000
## 1287 1.4007946 5.5 0.8509009 0.7404939 0.0000000
## 1288 1.4007946 5.5 0.8509009 0.7404939 0.0000000
## 1289 1.4007946 5.5 0.8509009 0.7404939 0.0000000
## 1290 1.4007946 5.5 0.8509009 0.7404939 0.0000000
## 1291 1.4007946 5.5 0.8509009 0.7404939 0.0000000
## 1292 1.4007946 5.5 0.8509009 0.7404939 0.0000000
## 1293 1.4007946 5.5 0.8509009 0.7404939 0.0000000
## 1294 1.4007946 5.5 0.8509009 0.7404939 0.0000000
## 1295 1.4007946 5.5 0.8509009 0.7404939 0.0000000
## 1296 1.4007946 5.5 0.8509009 0.7404939 0.0000000
## 1297 0.6574145 4.0 0.8908058 0.8031107 0.0000000
## 1298 0.6574145 4.0 0.8908058 0.8031107 0.0000000
## 1299 0.6574145 4.0 0.8908058 0.8031107 0.0000000
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## 1301 0.6574145 4.0 0.8908058 0.8031107 0.0000000
## 1302 0.6574145 4.0 0.8908058 0.8031107 0.0000000
## 1303 0.6574145 4.0 0.8908058 0.8031107 0.0000000
## 1304 0.6574145 4.0 0.8908058 0.8031107 0.0000000
## 1305 0.6574145 4.0 0.8908058 0.8031107 0.0000000
## 1306 0.6574145 4.0 0.8908058 0.8031107 0.0000000
## 1307 0.6574145 4.0 0.8908058 0.8031107 0.0000000
## 1308 0.6574145 4.0 0.8908058 0.8031107 0.0000000
## 1309 0.6574145 4.0 0.8908058 0.8031107 0.0000000
## 1310 0.6574145 4.0 0.8908058 0.8031107 0.0000000
## 1311 0.6574145 4.0 0.8908058 0.8031107 0.0000000
## 1312 0.6574145 4.0 0.8908058 0.8031107 0.0000000
## 1313 0.6574145 4.0 0.8908058 0.8031107 0.0000000
## 1314 0.6574145 4.0 0.8908058 0.8031107 0.0000000
## 1315 0.6574145 4.0 0.8908058 0.8031107 0.0000000
## 1316 0.6574145 4.0 0.8908058 0.8031107 0.0000000
## 1317 0.6574145 4.0 0.8908058 0.8031107 0.0000000
## 1318 0.6574145 4.0 0.8908058 0.8031107 0.0000000
## 1319 0.6574145 4.0 0.8908058 0.8031107 0.0000000
## 1320 0.6574145 4.0 0.8908058 0.8031107 0.0000000
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## 1322 0.7227886 4.0 0.9301829 0.8694784 0.0000000
## 1323 0.7227886 4.0 0.9301829 0.8694784 0.0000000
## 1324 0.7227886 4.0 0.9301829 0.8694784 0.0000000
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## 1326 0.7227886 4.0 0.9301829 0.8694784 0.0000000
## 1327 0.7227886 4.0 0.9301829 0.8694784 0.0000000
## 1328 0.7227886 4.0 0.9301829 0.8694784 0.0000000
## 1329 0.7227886 4.0 0.9301829 0.8694784 0.0000000
## 1330 0.7227886 4.0 0.9301829 0.8694784 0.0000000
## 1331 0.7227886 4.0 0.9301829 0.8694784 0.0000000
## 1332 0.7227886 4.0 0.9301829 0.8694784 0.0000000
## 1333 0.7227886 4.0 0.9301829 0.8694784 0.0000000
## 1334 0.7227886 4.0 0.9301829 0.8694784 0.0000000
## 1335 0.7227886 4.0 0.9301829 0.8694784 0.0000000
## 1336 0.7227886 4.0 0.9301829 0.8694784 0.0000000
## 1337 0.7227886 4.0 0.9301829 0.8694784 0.0000000
## 1338 0.7227886 4.0 0.9301829 0.8694784 0.0000000
## 1339 0.7227886 4.0 0.9301829 0.8694784 0.0000000
## 1340 0.7227886 4.0 0.9301829 0.8694784 0.0000000
## 1341 0.7227886 4.0 0.9301829 0.8694784 0.0000000
## 1342 0.7227886 4.0 0.9301829 0.8694784 0.0000000
## 1343 0.7227886 4.0 0.9301829 0.8694784 0.0000000
## 1344 0.7227886 4.0 0.9301829 0.8694784 0.0000000
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## 1347 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 1348 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 1349 2.0784670 11.0 0.0000000 0.0000000 0.0000000
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## 1352 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 1353 2.0784670 11.0 0.0000000 0.0000000 0.0000000
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## 1357 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 1358 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 1359 2.0784670 11.0 0.0000000 0.0000000 0.0000000
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## 1363 2.0784670 11.0 0.0000000 0.0000000 0.0000000
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## 1366 2.0784670 11.0 0.0000000 0.0000000 0.0000000
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## 1371 1.6890637 8.5 0.5151939 0.3469772 0.1250000
## 1372 1.6890637 8.5 0.5151939 0.3469772 0.1250000
## 1373 1.6890637 8.5 0.5151939 0.3469772 0.1250000
## 1374 1.6890637 8.5 0.5151939 0.3469772 0.1250000
## 1375 1.6890637 8.5 0.5151939 0.3469772 0.1250000
## 1376 1.6890637 8.5 0.5151939 0.3469772 0.1250000
## 1377 1.6890637 8.5 0.5151939 0.3469772 0.1250000
## 1378 1.6890637 8.5 0.5151939 0.3469772 0.1250000
## 1379 1.6890637 8.5 0.5151939 0.3469772 0.1250000
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## 1381 1.6890637 8.5 0.5151939 0.3469772 0.1250000
## 1382 1.6890637 8.5 0.5151939 0.3469772 0.1250000
## 1383 1.6890637 8.5 0.5151939 0.3469772 0.1250000
## 1384 1.6890637 8.5 0.5151939 0.3469772 0.1250000
## 1385 1.6890637 8.5 0.5151939 0.3469772 0.1250000
## 1386 1.6890637 8.5 0.5151939 0.3469772 0.1250000
## 1387 1.6890637 8.5 0.5151939 0.3469772 0.1250000
## 1388 1.6890637 8.5 0.5151939 0.3469772 0.1250000
## 1389 1.6890637 8.5 0.5151939 0.3469772 0.1250000
## 1390 1.6890637 8.5 0.5151939 0.3469772 0.1250000
## 1391 1.6890637 8.5 0.5151939 0.3469772 0.1250000
## 1392 1.6890637 8.5 0.5151939 0.3469772 0.1250000
## 1393 1.6613680 9.0 0.6067317 0.4354738 0.1111111
## 1394 1.6613680 9.0 0.6067317 0.4354738 0.1111111
## 1395 1.6613680 9.0 0.6067317 0.4354738 0.1111111
## 1396 1.6613680 9.0 0.6067317 0.4354738 0.1111111
## 1397 1.6613680 9.0 0.6067317 0.4354738 0.1111111
## 1398 1.6613680 9.0 0.6067317 0.4354738 0.1111111
## 1399 1.6613680 9.0 0.6067317 0.4354738 0.1111111
## 1400 1.6613680 9.0 0.6067317 0.4354738 0.1111111
## 1401 1.6613680 9.0 0.6067317 0.4354738 0.1111111
## 1402 1.6613680 9.0 0.6067317 0.4354738 0.1111111
## 1403 1.6613680 9.0 0.6067317 0.4354738 0.1111111
## 1404 1.6613680 9.0 0.6067317 0.4354738 0.1111111
## 1405 1.6613680 9.0 0.6067317 0.4354738 0.1111111
## 1406 1.6613680 9.0 0.6067317 0.4354738 0.1111111
## 1407 1.6613680 9.0 0.6067317 0.4354738 0.1111111
## 1408 1.6613680 9.0 0.6067317 0.4354738 0.1111111
## 1409 1.6613680 9.0 0.6067317 0.4354738 0.1111111
## 1410 1.6613680 9.0 0.6067317 0.4354738 0.1111111
## 1411 1.6613680 9.0 0.6067317 0.4354738 0.1111111
## 1412 1.6613680 9.0 0.6067317 0.4354738 0.1111111
## 1413 1.6613680 9.0 0.6067317 0.4354738 0.1111111
## 1414 1.6613680 9.0 0.6067317 0.4354738 0.1111111
## 1415 1.6613680 9.0 0.6067317 0.4354738 0.1111111
## 1416 1.6613680 9.0 0.6067317 0.4354738 0.1111111
## 1417 1.7836333 7.5 0.5057999 0.3385088 0.0000000
## 1418 1.7836333 7.5 0.5057999 0.3385088 0.0000000
## 1419 1.7836333 7.5 0.5057999 0.3385088 0.0000000
## 1420 1.7836333 7.5 0.5057999 0.3385088 0.0000000
## 1421 1.7836333 7.5 0.5057999 0.3385088 0.0000000
## 1422 1.7836333 7.5 0.5057999 0.3385088 0.0000000
## 1423 1.7836333 7.5 0.5057999 0.3385088 0.0000000
## 1424 1.7836333 7.5 0.5057999 0.3385088 0.0000000
## 1425 1.7836333 7.5 0.5057999 0.3385088 0.0000000
## 1426 1.7836333 7.5 0.5057999 0.3385088 0.0000000
## 1427 1.7836333 7.5 0.5057999 0.3385088 0.0000000
## 1428 1.7836333 7.5 0.5057999 0.3385088 0.0000000
## 1429 1.7836333 7.5 0.5057999 0.3385088 0.0000000
## 1430 1.7836333 7.5 0.5057999 0.3385088 0.0000000
## 1431 1.7836333 7.5 0.5057999 0.3385088 0.0000000
## 1432 1.7836333 7.5 0.5057999 0.3385088 0.0000000
## 1433 1.7836333 7.5 0.5057999 0.3385088 0.0000000
## 1434 1.7836333 7.5 0.5057999 0.3385088 0.0000000
## 1435 1.7836333 7.5 0.5057999 0.3385088 0.0000000
## 1436 1.7836333 7.5 0.5057999 0.3385088 0.0000000
## 1437 1.7836333 7.5 0.5057999 0.3385088 0.0000000
## 1438 1.7836333 7.5 0.5057999 0.3385088 0.0000000
## 1439 1.7836333 7.5 0.5057999 0.3385088 0.0000000
## 1440 1.7836333 7.5 0.5057999 0.3385088 0.0000000
## 1441 1.2942730 5.0 0.7453294 0.5940439 0.3333333
## 1442 1.2942730 5.0 0.7453294 0.5940439 0.3333333
## 1443 1.2942730 5.0 0.7453294 0.5940439 0.3333333
## 1444 1.2942730 5.0 0.7453294 0.5940439 0.3333333
## 1445 1.2942730 5.0 0.7453294 0.5940439 0.3333333
## 1446 1.2942730 5.0 0.7453294 0.5940439 0.3333333
## 1447 1.2942730 5.0 0.7453294 0.5940439 0.3333333
## 1448 1.2942730 5.0 0.7453294 0.5940439 0.3333333
## 1449 1.2942730 5.0 0.7453294 0.5940439 0.3333333
## 1450 1.2942730 5.0 0.7453294 0.5940439 0.3333333
## 1451 1.2942730 5.0 0.7453294 0.5940439 0.3333333
## 1452 1.2942730 5.0 0.7453294 0.5940439 0.3333333
## 1453 1.2942730 5.0 0.7453294 0.5940439 0.3333333
## 1454 1.2942730 5.0 0.7453294 0.5940439 0.3333333
## 1455 1.2942730 5.0 0.7453294 0.5940439 0.3333333
## 1456 1.2942730 5.0 0.7453294 0.5940439 0.3333333
## 1457 1.2942730 5.0 0.7453294 0.5940439 0.3333333
## 1458 1.2942730 5.0 0.7453294 0.5940439 0.3333333
## 1459 1.2942730 5.0 0.7453294 0.5940439 0.3333333
## 1460 1.2942730 5.0 0.7453294 0.5940439 0.3333333
## 1461 1.2942730 5.0 0.7453294 0.5940439 0.3333333
## 1462 1.2942730 5.0 0.7453294 0.5940439 0.3333333
## 1463 1.2942730 5.0 0.7453294 0.5940439 0.3333333
## 1464 1.2942730 5.0 0.7453294 0.5940439 0.3333333
## 1465 1.2686511 4.5 0.9064490 0.8289043 0.3333333
## 1466 1.2686511 4.5 0.9064490 0.8289043 0.3333333
## 1467 1.2686511 4.5 0.9064490 0.8289043 0.3333333
## 1468 1.2686511 4.5 0.9064490 0.8289043 0.3333333
## 1469 1.2686511 4.5 0.9064490 0.8289043 0.3333333
## 1470 1.2686511 4.5 0.9064490 0.8289043 0.3333333
## 1471 1.2686511 4.5 0.9064490 0.8289043 0.3333333
## 1472 1.2686511 4.5 0.9064490 0.8289043 0.3333333
## 1473 1.2686511 4.5 0.9064490 0.8289043 0.3333333
## 1474 1.2686511 4.5 0.9064490 0.8289043 0.3333333
## 1475 1.2686511 4.5 0.9064490 0.8289043 0.3333333
## 1476 1.2686511 4.5 0.9064490 0.8289043 0.3333333
## 1477 1.2686511 4.5 0.9064490 0.8289043 0.3333333
## 1478 1.2686511 4.5 0.9064490 0.8289043 0.3333333
## 1479 1.2686511 4.5 0.9064490 0.8289043 0.3333333
## 1480 1.2686511 4.5 0.9064490 0.8289043 0.3333333
## 1481 1.2686511 4.5 0.9064490 0.8289043 0.3333333
## 1482 1.2686511 4.5 0.9064490 0.8289043 0.3333333
## 1483 1.2686511 4.5 0.9064490 0.8289043 0.3333333
## 1484 1.2686511 4.5 0.9064490 0.8289043 0.3333333
## 1485 1.2686511 4.5 0.9064490 0.8289043 0.3333333
## 1486 1.2686511 4.5 0.9064490 0.8289043 0.3333333
## 1487 1.2686511 4.5 0.9064490 0.8289043 0.3333333
## 1488 1.2686511 4.5 0.9064490 0.8289043 0.3333333
## 1489 0.5707230 4.5 0.8832972 0.7909868 0.0000000
## 1490 0.5707230 4.5 0.8832972 0.7909868 0.0000000
## 1491 0.5707230 4.5 0.8832972 0.7909868 0.0000000
## 1492 0.5707230 4.5 0.8832972 0.7909868 0.0000000
## 1493 0.5707230 4.5 0.8832972 0.7909868 0.0000000
## 1494 0.5707230 4.5 0.8832972 0.7909868 0.0000000
## 1495 0.5707230 4.5 0.8832972 0.7909868 0.0000000
## 1496 0.5707230 4.5 0.8832972 0.7909868 0.0000000
## 1497 0.5707230 4.5 0.8832972 0.7909868 0.0000000
## 1498 0.5707230 4.5 0.8832972 0.7909868 0.0000000
## 1499 0.5707230 4.5 0.8832972 0.7909868 0.0000000
## 1500 0.5707230 4.5 0.8832972 0.7909868 0.0000000
## 1501 0.5707230 4.5 0.8832972 0.7909868 0.0000000
## 1502 0.5707230 4.5 0.8832972 0.7909868 0.0000000
## 1503 0.5707230 4.5 0.8832972 0.7909868 0.0000000
## 1504 0.5707230 4.5 0.8832972 0.7909868 0.0000000
## 1505 0.5707230 4.5 0.8832972 0.7909868 0.0000000
## 1506 0.5707230 4.5 0.8832972 0.7909868 0.0000000
## 1507 0.5707230 4.5 0.8832972 0.7909868 0.0000000
## 1508 0.5707230 4.5 0.8832972 0.7909868 0.0000000
## 1509 0.5707230 4.5 0.8832972 0.7909868 0.0000000
## 1510 0.5707230 4.5 0.8832972 0.7909868 0.0000000
## 1511 0.5707230 4.5 0.8832972 0.7909868 0.0000000
## 1512 0.5707230 4.5 0.8832972 0.7909868 0.0000000
## 1513 0.5951247 4.0 0.9576002 0.9186497 0.0000000
## 1514 0.5951247 4.0 0.9576002 0.9186497 0.0000000
## 1515 0.5951247 4.0 0.9576002 0.9186497 0.0000000
## 1516 0.5951247 4.0 0.9576002 0.9186497 0.0000000
## 1517 0.5951247 4.0 0.9576002 0.9186497 0.0000000
## 1518 0.5951247 4.0 0.9576002 0.9186497 0.0000000
## 1519 0.5951247 4.0 0.9576002 0.9186497 0.0000000
## 1520 0.5951247 4.0 0.9576002 0.9186497 0.0000000
## 1521 0.5951247 4.0 0.9576002 0.9186497 0.0000000
## 1522 0.5951247 4.0 0.9576002 0.9186497 0.0000000
## 1523 0.5951247 4.0 0.9576002 0.9186497 0.0000000
## 1524 0.5951247 4.0 0.9576002 0.9186497 0.0000000
## 1525 0.5951247 4.0 0.9576002 0.9186497 0.0000000
## 1526 0.5951247 4.0 0.9576002 0.9186497 0.0000000
## 1527 0.5951247 4.0 0.9576002 0.9186497 0.0000000
## 1528 0.5951247 4.0 0.9576002 0.9186497 0.0000000
## 1529 0.5951247 4.0 0.9576002 0.9186497 0.0000000
## 1530 0.5951247 4.0 0.9576002 0.9186497 0.0000000
## 1531 0.5951247 4.0 0.9576002 0.9186497 0.0000000
## 1532 0.5951247 4.0 0.9576002 0.9186497 0.0000000
## 1533 0.5951247 4.0 0.9576002 0.9186497 0.0000000
## 1534 0.5951247 4.0 0.9576002 0.9186497 0.0000000
## 1535 0.5951247 4.0 0.9576002 0.9186497 0.0000000
## 1536 0.5951247 4.0 0.9576002 0.9186497 0.0000000
## 1537 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 1538 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 1539 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 1540 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 1541 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 1542 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 1543 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 1544 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 1545 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 1546 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 1547 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 1548 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 1549 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 1550 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 1551 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 1552 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 1553 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 1554 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 1555 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 1556 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 1557 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 1558 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 1559 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 1560 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 1561 1.5862113 7.5 0.3935303 0.2449659 0.1428571
## 1562 1.5862113 7.5 0.3935303 0.2449659 0.1428571
## 1563 1.5862113 7.5 0.3935303 0.2449659 0.1428571
## 1564 1.5862113 7.5 0.3935303 0.2449659 0.1428571
## 1565 1.5862113 7.5 0.3935303 0.2449659 0.1428571
## 1566 1.5862113 7.5 0.3935303 0.2449659 0.1428571
## 1567 1.5862113 7.5 0.3935303 0.2449659 0.1428571
## 1568 1.5862113 7.5 0.3935303 0.2449659 0.1428571
## 1569 1.5862113 7.5 0.3935303 0.2449659 0.1428571
## 1570 1.5862113 7.5 0.3935303 0.2449659 0.1428571
## 1571 1.5862113 7.5 0.3935303 0.2449659 0.1428571
## 1572 1.5862113 7.5 0.3935303 0.2449659 0.1428571
## 1573 1.5862113 7.5 0.3935303 0.2449659 0.1428571
## 1574 1.5862113 7.5 0.3935303 0.2449659 0.1428571
## 1575 1.5862113 7.5 0.3935303 0.2449659 0.1428571
## 1576 1.5862113 7.5 0.3935303 0.2449659 0.1428571
## 1577 1.5862113 7.5 0.3935303 0.2449659 0.1428571
## 1578 1.5862113 7.5 0.3935303 0.2449659 0.1428571
## 1579 1.5862113 7.5 0.3935303 0.2449659 0.1428571
## 1580 1.5862113 7.5 0.3935303 0.2449659 0.1428571
## 1581 1.5862113 7.5 0.3935303 0.2449659 0.1428571
## 1582 1.5862113 7.5 0.3935303 0.2449659 0.1428571
## 1583 1.5862113 7.5 0.3935303 0.2449659 0.1428571
## 1584 1.5862113 7.5 0.3935303 0.2449659 0.1428571
## 1585 1.4948352 8.5 0.6102781 0.4391368 0.1250000
## 1586 1.4948352 8.5 0.6102781 0.4391368 0.1250000
## 1587 1.4948352 8.5 0.6102781 0.4391368 0.1250000
## 1588 1.4948352 8.5 0.6102781 0.4391368 0.1250000
## 1589 1.4948352 8.5 0.6102781 0.4391368 0.1250000
## 1590 1.4948352 8.5 0.6102781 0.4391368 0.1250000
## 1591 1.4948352 8.5 0.6102781 0.4391368 0.1250000
## 1592 1.4948352 8.5 0.6102781 0.4391368 0.1250000
## 1593 1.4948352 8.5 0.6102781 0.4391368 0.1250000
## 1594 1.4948352 8.5 0.6102781 0.4391368 0.1250000
## 1595 1.4948352 8.5 0.6102781 0.4391368 0.1250000
## 1596 1.4948352 8.5 0.6102781 0.4391368 0.1250000
## 1597 1.4948352 8.5 0.6102781 0.4391368 0.1250000
## 1598 1.4948352 8.5 0.6102781 0.4391368 0.1250000
## 1599 1.4948352 8.5 0.6102781 0.4391368 0.1250000
## 1600 1.4948352 8.5 0.6102781 0.4391368 0.1250000
## 1601 1.4948352 8.5 0.6102781 0.4391368 0.1250000
## 1602 1.4948352 8.5 0.6102781 0.4391368 0.1250000
## 1603 1.4948352 8.5 0.6102781 0.4391368 0.1250000
## 1604 1.4948352 8.5 0.6102781 0.4391368 0.1250000
## 1605 1.4948352 8.5 0.6102781 0.4391368 0.1250000
## 1606 1.4948352 8.5 0.6102781 0.4391368 0.1250000
## 1607 1.4948352 8.5 0.6102781 0.4391368 0.1250000
## 1608 1.4948352 8.5 0.6102781 0.4391368 0.1250000
## 1609 1.4254256 6.0 0.5077328 0.3402426 0.1666667
## 1610 1.4254256 6.0 0.5077328 0.3402426 0.1666667
## 1611 1.4254256 6.0 0.5077328 0.3402426 0.1666667
## 1612 1.4254256 6.0 0.5077328 0.3402426 0.1666667
## 1613 1.4254256 6.0 0.5077328 0.3402426 0.1666667
## 1614 1.4254256 6.0 0.5077328 0.3402426 0.1666667
## 1615 1.4254256 6.0 0.5077328 0.3402426 0.1666667
## 1616 1.4254256 6.0 0.5077328 0.3402426 0.1666667
## 1617 1.4254256 6.0 0.5077328 0.3402426 0.1666667
## 1618 1.4254256 6.0 0.5077328 0.3402426 0.1666667
## 1619 1.4254256 6.0 0.5077328 0.3402426 0.1666667
## 1620 1.4254256 6.0 0.5077328 0.3402426 0.1666667
## 1621 1.4254256 6.0 0.5077328 0.3402426 0.1666667
## 1622 1.4254256 6.0 0.5077328 0.3402426 0.1666667
## 1623 1.4254256 6.0 0.5077328 0.3402426 0.1666667
## 1624 1.4254256 6.0 0.5077328 0.3402426 0.1666667
## 1625 1.4254256 6.0 0.5077328 0.3402426 0.1666667
## 1626 1.4254256 6.0 0.5077328 0.3402426 0.1666667
## 1627 1.4254256 6.0 0.5077328 0.3402426 0.1666667
## 1628 1.4254256 6.0 0.5077328 0.3402426 0.1666667
## 1629 1.4254256 6.0 0.5077328 0.3402426 0.1666667
## 1630 1.4254256 6.0 0.5077328 0.3402426 0.1666667
## 1631 1.4254256 6.0 0.5077328 0.3402426 0.1666667
## 1632 1.4254256 6.0 0.5077328 0.3402426 0.1666667
## 1633 1.1525871 4.5 0.7699053 0.6258911 0.6666667
## 1634 1.1525871 4.5 0.7699053 0.6258911 0.6666667
## 1635 1.1525871 4.5 0.7699053 0.6258911 0.6666667
## 1636 1.1525871 4.5 0.7699053 0.6258911 0.6666667
## 1637 1.1525871 4.5 0.7699053 0.6258911 0.6666667
## 1638 1.1525871 4.5 0.7699053 0.6258911 0.6666667
## 1639 1.1525871 4.5 0.7699053 0.6258911 0.6666667
## 1640 1.1525871 4.5 0.7699053 0.6258911 0.6666667
## 1641 1.1525871 4.5 0.7699053 0.6258911 0.6666667
## 1642 1.1525871 4.5 0.7699053 0.6258911 0.6666667
## 1643 1.1525871 4.5 0.7699053 0.6258911 0.6666667
## 1644 1.1525871 4.5 0.7699053 0.6258911 0.6666667
## 1645 1.1525871 4.5 0.7699053 0.6258911 0.6666667
## 1646 1.1525871 4.5 0.7699053 0.6258911 0.6666667
## 1647 1.1525871 4.5 0.7699053 0.6258911 0.6666667
## 1648 1.1525871 4.5 0.7699053 0.6258911 0.6666667
## 1649 1.1525871 4.5 0.7699053 0.6258911 0.6666667
## 1650 1.1525871 4.5 0.7699053 0.6258911 0.6666667
## 1651 1.1525871 4.5 0.7699053 0.6258911 0.6666667
## 1652 1.1525871 4.5 0.7699053 0.6258911 0.6666667
## 1653 1.1525871 4.5 0.7699053 0.6258911 0.6666667
## 1654 1.1525871 4.5 0.7699053 0.6258911 0.6666667
## 1655 1.1525871 4.5 0.7699053 0.6258911 0.6666667
## 1656 1.1525871 4.5 0.7699053 0.6258911 0.6666667
## 1657 1.2474031 4.0 0.9374289 0.8822270 0.6666667
## 1658 1.2474031 4.0 0.9374289 0.8822270 0.6666667
## 1659 1.2474031 4.0 0.9374289 0.8822270 0.6666667
## 1660 1.2474031 4.0 0.9374289 0.8822270 0.6666667
## 1661 1.2474031 4.0 0.9374289 0.8822270 0.6666667
## 1662 1.2474031 4.0 0.9374289 0.8822270 0.6666667
## 1663 1.2474031 4.0 0.9374289 0.8822270 0.6666667
## 1664 1.2474031 4.0 0.9374289 0.8822270 0.6666667
## 1665 1.2474031 4.0 0.9374289 0.8822270 0.6666667
## 1666 1.2474031 4.0 0.9374289 0.8822270 0.6666667
## 1667 1.2474031 4.0 0.9374289 0.8822270 0.6666667
## 1668 1.2474031 4.0 0.9374289 0.8822270 0.6666667
## 1669 1.2474031 4.0 0.9374289 0.8822270 0.6666667
## 1670 1.2474031 4.0 0.9374289 0.8822270 0.6666667
## 1671 1.2474031 4.0 0.9374289 0.8822270 0.6666667
## 1672 1.2474031 4.0 0.9374289 0.8822270 0.6666667
## 1673 1.2474031 4.0 0.9374289 0.8822270 0.6666667
## 1674 1.2474031 4.0 0.9374289 0.8822270 0.6666667
## 1675 1.2474031 4.0 0.9374289 0.8822270 0.6666667
## 1676 1.2474031 4.0 0.9374289 0.8822270 0.6666667
## 1677 1.2474031 4.0 0.9374289 0.8822270 0.6666667
## 1678 1.2474031 4.0 0.9374289 0.8822270 0.6666667
## 1679 1.2474031 4.0 0.9374289 0.8822270 0.6666667
## 1680 1.2474031 4.0 0.9374289 0.8822270 0.6666667
## 1681 0.7728167 3.5 0.7388350 0.5858353 0.0000000
## 1682 0.7728167 3.5 0.7388350 0.5858353 0.0000000
## 1683 0.7728167 3.5 0.7388350 0.5858353 0.0000000
## 1684 0.7728167 3.5 0.7388350 0.5858353 0.0000000
## 1685 0.7728167 3.5 0.7388350 0.5858353 0.0000000
## 1686 0.7728167 3.5 0.7388350 0.5858353 0.0000000
## 1687 0.7728167 3.5 0.7388350 0.5858353 0.0000000
## 1688 0.7728167 3.5 0.7388350 0.5858353 0.0000000
## 1689 0.7728167 3.5 0.7388350 0.5858353 0.0000000
## 1690 0.7728167 3.5 0.7388350 0.5858353 0.0000000
## 1691 0.7728167 3.5 0.7388350 0.5858353 0.0000000
## 1692 0.7728167 3.5 0.7388350 0.5858353 0.0000000
## 1693 0.7728167 3.5 0.7388350 0.5858353 0.0000000
## 1694 0.7728167 3.5 0.7388350 0.5858353 0.0000000
## 1695 0.7728167 3.5 0.7388350 0.5858353 0.0000000
## 1696 0.7728167 3.5 0.7388350 0.5858353 0.0000000
## 1697 0.7728167 3.5 0.7388350 0.5858353 0.0000000
## 1698 0.7728167 3.5 0.7388350 0.5858353 0.0000000
## 1699 0.7728167 3.5 0.7388350 0.5858353 0.0000000
## 1700 0.7728167 3.5 0.7388350 0.5858353 0.0000000
## 1701 0.7728167 3.5 0.7388350 0.5858353 0.0000000
## 1702 0.7728167 3.5 0.7388350 0.5858353 0.0000000
## 1703 0.7728167 3.5 0.7388350 0.5858353 0.0000000
## 1704 0.7728167 3.5 0.7388350 0.5858353 0.0000000
## 1705 0.6460517 3.0 0.7497872 0.5997277 0.0000000
## 1706 0.6460517 3.0 0.7497872 0.5997277 0.0000000
## 1707 0.6460517 3.0 0.7497872 0.5997277 0.0000000
## 1708 0.6460517 3.0 0.7497872 0.5997277 0.0000000
## 1709 0.6460517 3.0 0.7497872 0.5997277 0.0000000
## 1710 0.6460517 3.0 0.7497872 0.5997277 0.0000000
## 1711 0.6460517 3.0 0.7497872 0.5997277 0.0000000
## 1712 0.6460517 3.0 0.7497872 0.5997277 0.0000000
## 1713 0.6460517 3.0 0.7497872 0.5997277 0.0000000
## 1714 0.6460517 3.0 0.7497872 0.5997277 0.0000000
## 1715 0.6460517 3.0 0.7497872 0.5997277 0.0000000
## 1716 0.6460517 3.0 0.7497872 0.5997277 0.0000000
## 1717 0.6460517 3.0 0.7497872 0.5997277 0.0000000
## 1718 0.6460517 3.0 0.7497872 0.5997277 0.0000000
## 1719 0.6460517 3.0 0.7497872 0.5997277 0.0000000
## 1720 0.6460517 3.0 0.7497872 0.5997277 0.0000000
## 1721 0.6460517 3.0 0.7497872 0.5997277 0.0000000
## 1722 0.6460517 3.0 0.7497872 0.5997277 0.0000000
## 1723 0.6460517 3.0 0.7497872 0.5997277 0.0000000
## 1724 0.6460517 3.0 0.7497872 0.5997277 0.0000000
## 1725 0.6460517 3.0 0.7497872 0.5997277 0.0000000
## 1726 0.6460517 3.0 0.7497872 0.5997277 0.0000000
## 1727 0.6460517 3.0 0.7497872 0.5997277 0.0000000
## 1728 0.6460517 3.0 0.7497872 0.5997277 0.0000000
## 1729 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 1730 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 1731 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 1732 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 1733 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 1734 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 1735 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 1736 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 1737 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 1738 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 1739 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 1740 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 1741 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 1742 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 1743 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 1744 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 1745 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 1746 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 1747 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 1748 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 1749 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 1750 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 1751 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 1752 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 1753 1.8112725 8.5 0.5241108 0.3551153 0.0000000
## 1754 1.8112725 8.5 0.5241108 0.3551153 0.0000000
## 1755 1.8112725 8.5 0.5241108 0.3551153 0.0000000
## 1756 1.8112725 8.5 0.5241108 0.3551153 0.0000000
## 1757 1.8112725 8.5 0.5241108 0.3551153 0.0000000
## 1758 1.8112725 8.5 0.5241108 0.3551153 0.0000000
## 1759 1.8112725 8.5 0.5241108 0.3551153 0.0000000
## 1760 1.8112725 8.5 0.5241108 0.3551153 0.0000000
## 1761 1.8112725 8.5 0.5241108 0.3551153 0.0000000
## 1762 1.8112725 8.5 0.5241108 0.3551153 0.0000000
## 1763 1.8112725 8.5 0.5241108 0.3551153 0.0000000
## 1764 1.8112725 8.5 0.5241108 0.3551153 0.0000000
## 1765 1.8112725 8.5 0.5241108 0.3551153 0.0000000
## 1766 1.8112725 8.5 0.5241108 0.3551153 0.0000000
## 1767 1.8112725 8.5 0.5241108 0.3551153 0.0000000
## 1768 1.8112725 8.5 0.5241108 0.3551153 0.0000000
## 1769 1.8112725 8.5 0.5241108 0.3551153 0.0000000
## 1770 1.8112725 8.5 0.5241108 0.3551153 0.0000000
## 1771 1.8112725 8.5 0.5241108 0.3551153 0.0000000
## 1772 1.8112725 8.5 0.5241108 0.3551153 0.0000000
## 1773 1.8112725 8.5 0.5241108 0.3551153 0.0000000
## 1774 1.8112725 8.5 0.5241108 0.3551153 0.0000000
## 1775 1.8112725 8.5 0.5241108 0.3551153 0.0000000
## 1776 1.8112725 8.5 0.5241108 0.3551153 0.0000000
## 1777 1.5778693 8.0 0.7006088 0.5391824 0.1428571
## 1778 1.5778693 8.0 0.7006088 0.5391824 0.1428571
## 1779 1.5778693 8.0 0.7006088 0.5391824 0.1428571
## 1780 1.5778693 8.0 0.7006088 0.5391824 0.1428571
## 1781 1.5778693 8.0 0.7006088 0.5391824 0.1428571
## 1782 1.5778693 8.0 0.7006088 0.5391824 0.1428571
## 1783 1.5778693 8.0 0.7006088 0.5391824 0.1428571
## 1784 1.5778693 8.0 0.7006088 0.5391824 0.1428571
## 1785 1.5778693 8.0 0.7006088 0.5391824 0.1428571
## 1786 1.5778693 8.0 0.7006088 0.5391824 0.1428571
## 1787 1.5778693 8.0 0.7006088 0.5391824 0.1428571
## 1788 1.5778693 8.0 0.7006088 0.5391824 0.1428571
## 1789 1.5778693 8.0 0.7006088 0.5391824 0.1428571
## 1790 1.5778693 8.0 0.7006088 0.5391824 0.1428571
## 1791 1.5778693 8.0 0.7006088 0.5391824 0.1428571
## 1792 1.5778693 8.0 0.7006088 0.5391824 0.1428571
## 1793 1.5778693 8.0 0.7006088 0.5391824 0.1428571
## 1794 1.5778693 8.0 0.7006088 0.5391824 0.1428571
## 1795 1.5778693 8.0 0.7006088 0.5391824 0.1428571
## 1796 1.5778693 8.0 0.7006088 0.5391824 0.1428571
## 1797 1.5778693 8.0 0.7006088 0.5391824 0.1428571
## 1798 1.5778693 8.0 0.7006088 0.5391824 0.1428571
## 1799 1.5778693 8.0 0.7006088 0.5391824 0.1428571
## 1800 1.5778693 8.0 0.7006088 0.5391824 0.1428571
## 1801 1.7398154 7.0 0.4756098 0.3120000 0.0000000
## 1802 1.7398154 7.0 0.4756098 0.3120000 0.0000000
## 1803 1.7398154 7.0 0.4756098 0.3120000 0.0000000
## 1804 1.7398154 7.0 0.4756098 0.3120000 0.0000000
## 1805 1.7398154 7.0 0.4756098 0.3120000 0.0000000
## 1806 1.7398154 7.0 0.4756098 0.3120000 0.0000000
## 1807 1.7398154 7.0 0.4756098 0.3120000 0.0000000
## 1808 1.7398154 7.0 0.4756098 0.3120000 0.0000000
## 1809 1.7398154 7.0 0.4756098 0.3120000 0.0000000
## 1810 1.7398154 7.0 0.4756098 0.3120000 0.0000000
## 1811 1.7398154 7.0 0.4756098 0.3120000 0.0000000
## 1812 1.7398154 7.0 0.4756098 0.3120000 0.0000000
## 1813 1.7398154 7.0 0.4756098 0.3120000 0.0000000
## 1814 1.7398154 7.0 0.4756098 0.3120000 0.0000000
## 1815 1.7398154 7.0 0.4756098 0.3120000 0.0000000
## 1816 1.7398154 7.0 0.4756098 0.3120000 0.0000000
## 1817 1.7398154 7.0 0.4756098 0.3120000 0.0000000
## 1818 1.7398154 7.0 0.4756098 0.3120000 0.0000000
## 1819 1.7398154 7.0 0.4756098 0.3120000 0.0000000
## 1820 1.7398154 7.0 0.4756098 0.3120000 0.0000000
## 1821 1.7398154 7.0 0.4756098 0.3120000 0.0000000
## 1822 1.7398154 7.0 0.4756098 0.3120000 0.0000000
## 1823 1.7398154 7.0 0.4756098 0.3120000 0.0000000
## 1824 1.7398154 7.0 0.4756098 0.3120000 0.0000000
## 1825 1.2519825 5.5 0.7460988 0.5950220 0.3333333
## 1826 1.2519825 5.5 0.7460988 0.5950220 0.3333333
## 1827 1.2519825 5.5 0.7460988 0.5950220 0.3333333
## 1828 1.2519825 5.5 0.7460988 0.5950220 0.3333333
## 1829 1.2519825 5.5 0.7460988 0.5950220 0.3333333
## 1830 1.2519825 5.5 0.7460988 0.5950220 0.3333333
## 1831 1.2519825 5.5 0.7460988 0.5950220 0.3333333
## 1832 1.2519825 5.5 0.7460988 0.5950220 0.3333333
## 1833 1.2519825 5.5 0.7460988 0.5950220 0.3333333
## 1834 1.2519825 5.5 0.7460988 0.5950220 0.3333333
## 1835 1.2519825 5.5 0.7460988 0.5950220 0.3333333
## 1836 1.2519825 5.5 0.7460988 0.5950220 0.3333333
## 1837 1.2519825 5.5 0.7460988 0.5950220 0.3333333
## 1838 1.2519825 5.5 0.7460988 0.5950220 0.3333333
## 1839 1.2519825 5.5 0.7460988 0.5950220 0.3333333
## 1840 1.2519825 5.5 0.7460988 0.5950220 0.3333333
## 1841 1.2519825 5.5 0.7460988 0.5950220 0.3333333
## 1842 1.2519825 5.5 0.7460988 0.5950220 0.3333333
## 1843 1.2519825 5.5 0.7460988 0.5950220 0.3333333
## 1844 1.2519825 5.5 0.7460988 0.5950220 0.3333333
## 1845 1.2519825 5.5 0.7460988 0.5950220 0.3333333
## 1846 1.2519825 5.5 0.7460988 0.5950220 0.3333333
## 1847 1.2519825 5.5 0.7460988 0.5950220 0.3333333
## 1848 1.2519825 5.5 0.7460988 0.5950220 0.3333333
## 1849 1.4667712 5.5 0.9079875 0.8314808 0.3333333
## 1850 1.4667712 5.5 0.9079875 0.8314808 0.3333333
## 1851 1.4667712 5.5 0.9079875 0.8314808 0.3333333
## 1852 1.4667712 5.5 0.9079875 0.8314808 0.3333333
## 1853 1.4667712 5.5 0.9079875 0.8314808 0.3333333
## 1854 1.4667712 5.5 0.9079875 0.8314808 0.3333333
## 1855 1.4667712 5.5 0.9079875 0.8314808 0.3333333
## 1856 1.4667712 5.5 0.9079875 0.8314808 0.3333333
## 1857 1.4667712 5.5 0.9079875 0.8314808 0.3333333
## 1858 1.4667712 5.5 0.9079875 0.8314808 0.3333333
## 1859 1.4667712 5.5 0.9079875 0.8314808 0.3333333
## 1860 1.4667712 5.5 0.9079875 0.8314808 0.3333333
## 1861 1.4667712 5.5 0.9079875 0.8314808 0.3333333
## 1862 1.4667712 5.5 0.9079875 0.8314808 0.3333333
## 1863 1.4667712 5.5 0.9079875 0.8314808 0.3333333
## 1864 1.4667712 5.5 0.9079875 0.8314808 0.3333333
## 1865 1.4667712 5.5 0.9079875 0.8314808 0.3333333
## 1866 1.4667712 5.5 0.9079875 0.8314808 0.3333333
## 1867 1.4667712 5.5 0.9079875 0.8314808 0.3333333
## 1868 1.4667712 5.5 0.9079875 0.8314808 0.3333333
## 1869 1.4667712 5.5 0.9079875 0.8314808 0.3333333
## 1870 1.4667712 5.5 0.9079875 0.8314808 0.3333333
## 1871 1.4667712 5.5 0.9079875 0.8314808 0.3333333
## 1872 1.4667712 5.5 0.9079875 0.8314808 0.3333333
## 1873 0.8928972 4.5 0.9027828 0.8227931 0.0000000
## 1874 0.8928972 4.5 0.9027828 0.8227931 0.0000000
## 1875 0.8928972 4.5 0.9027828 0.8227931 0.0000000
## 1876 0.8928972 4.5 0.9027828 0.8227931 0.0000000
## 1877 0.8928972 4.5 0.9027828 0.8227931 0.0000000
## 1878 0.8928972 4.5 0.9027828 0.8227931 0.0000000
## 1879 0.8928972 4.5 0.9027828 0.8227931 0.0000000
## 1880 0.8928972 4.5 0.9027828 0.8227931 0.0000000
## 1881 0.8928972 4.5 0.9027828 0.8227931 0.0000000
## 1882 0.8928972 4.5 0.9027828 0.8227931 0.0000000
## 1883 0.8928972 4.5 0.9027828 0.8227931 0.0000000
## 1884 0.8928972 4.5 0.9027828 0.8227931 0.0000000
## 1885 0.8928972 4.5 0.9027828 0.8227931 0.0000000
## 1886 0.8928972 4.5 0.9027828 0.8227931 0.0000000
## 1887 0.8928972 4.5 0.9027828 0.8227931 0.0000000
## 1888 0.8928972 4.5 0.9027828 0.8227931 0.0000000
## 1889 0.8928972 4.5 0.9027828 0.8227931 0.0000000
## 1890 0.8928972 4.5 0.9027828 0.8227931 0.0000000
## 1891 0.8928972 4.5 0.9027828 0.8227931 0.0000000
## 1892 0.8928972 4.5 0.9027828 0.8227931 0.0000000
## 1893 0.8928972 4.5 0.9027828 0.8227931 0.0000000
## 1894 0.8928972 4.5 0.9027828 0.8227931 0.0000000
## 1895 0.8928972 4.5 0.9027828 0.8227931 0.0000000
## 1896 0.8928972 4.5 0.9027828 0.8227931 0.0000000
## 1897 0.6549539 4.5 0.9497693 0.9043436 0.0000000
## 1898 0.6549539 4.5 0.9497693 0.9043436 0.0000000
## 1899 0.6549539 4.5 0.9497693 0.9043436 0.0000000
## 1900 0.6549539 4.5 0.9497693 0.9043436 0.0000000
## 1901 0.6549539 4.5 0.9497693 0.9043436 0.0000000
## 1902 0.6549539 4.5 0.9497693 0.9043436 0.0000000
## 1903 0.6549539 4.5 0.9497693 0.9043436 0.0000000
## 1904 0.6549539 4.5 0.9497693 0.9043436 0.0000000
## 1905 0.6549539 4.5 0.9497693 0.9043436 0.0000000
## 1906 0.6549539 4.5 0.9497693 0.9043436 0.0000000
## 1907 0.6549539 4.5 0.9497693 0.9043436 0.0000000
## 1908 0.6549539 4.5 0.9497693 0.9043436 0.0000000
## 1909 0.6549539 4.5 0.9497693 0.9043436 0.0000000
## 1910 0.6549539 4.5 0.9497693 0.9043436 0.0000000
## 1911 0.6549539 4.5 0.9497693 0.9043436 0.0000000
## 1912 0.6549539 4.5 0.9497693 0.9043436 0.0000000
## 1913 0.6549539 4.5 0.9497693 0.9043436 0.0000000
## 1914 0.6549539 4.5 0.9497693 0.9043436 0.0000000
## 1915 0.6549539 4.5 0.9497693 0.9043436 0.0000000
## 1916 0.6549539 4.5 0.9497693 0.9043436 0.0000000
## 1917 0.6549539 4.5 0.9497693 0.9043436 0.0000000
## 1918 0.6549539 4.5 0.9497693 0.9043436 0.0000000
## 1919 0.6549539 4.5 0.9497693 0.9043436 0.0000000
## 1920 0.6549539 4.5 0.9497693 0.9043436 0.0000000
## 1921 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 1922 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 1923 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 1924 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 1925 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 1926 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 1927 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 1928 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 1929 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 1930 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 1931 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 1932 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 1933 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 1934 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 1935 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 1936 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 1937 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 1938 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 1939 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 1940 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 1941 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 1942 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 1943 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 1944 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 1945 1.4587022 7.0 0.4647482 0.3027179 0.0000000
## 1946 1.4587022 7.0 0.4647482 0.3027179 0.0000000
## 1947 1.4587022 7.0 0.4647482 0.3027179 0.0000000
## 1948 1.4587022 7.0 0.4647482 0.3027179 0.0000000
## 1949 1.4587022 7.0 0.4647482 0.3027179 0.0000000
## 1950 1.4587022 7.0 0.4647482 0.3027179 0.0000000
## 1951 1.4587022 7.0 0.4647482 0.3027179 0.0000000
## 1952 1.4587022 7.0 0.4647482 0.3027179 0.0000000
## 1953 1.4587022 7.0 0.4647482 0.3027179 0.0000000
## 1954 1.4587022 7.0 0.4647482 0.3027179 0.0000000
## 1955 1.4587022 7.0 0.4647482 0.3027179 0.0000000
## 1956 1.4587022 7.0 0.4647482 0.3027179 0.0000000
## 1957 1.4587022 7.0 0.4647482 0.3027179 0.0000000
## 1958 1.4587022 7.0 0.4647482 0.3027179 0.0000000
## 1959 1.4587022 7.0 0.4647482 0.3027179 0.0000000
## 1960 1.4587022 7.0 0.4647482 0.3027179 0.0000000
## 1961 1.4587022 7.0 0.4647482 0.3027179 0.0000000
## 1962 1.4587022 7.0 0.4647482 0.3027179 0.0000000
## 1963 1.4587022 7.0 0.4647482 0.3027179 0.0000000
## 1964 1.4587022 7.0 0.4647482 0.3027179 0.0000000
## 1965 1.4587022 7.0 0.4647482 0.3027179 0.0000000
## 1966 1.4587022 7.0 0.4647482 0.3027179 0.0000000
## 1967 1.4587022 7.0 0.4647482 0.3027179 0.0000000
## 1968 1.4587022 7.0 0.4647482 0.3027179 0.0000000
## 1969 1.4635307 7.0 0.5222681 0.3534255 0.0000000
## 1970 1.4635307 7.0 0.5222681 0.3534255 0.0000000
## 1971 1.4635307 7.0 0.5222681 0.3534255 0.0000000
## 1972 1.4635307 7.0 0.5222681 0.3534255 0.0000000
## 1973 1.4635307 7.0 0.5222681 0.3534255 0.0000000
## 1974 1.4635307 7.0 0.5222681 0.3534255 0.0000000
## 1975 1.4635307 7.0 0.5222681 0.3534255 0.0000000
## 1976 1.4635307 7.0 0.5222681 0.3534255 0.0000000
## 1977 1.4635307 7.0 0.5222681 0.3534255 0.0000000
## 1978 1.4635307 7.0 0.5222681 0.3534255 0.0000000
## 1979 1.4635307 7.0 0.5222681 0.3534255 0.0000000
## 1980 1.4635307 7.0 0.5222681 0.3534255 0.0000000
## 1981 1.4635307 7.0 0.5222681 0.3534255 0.0000000
## 1982 1.4635307 7.0 0.5222681 0.3534255 0.0000000
## 1983 1.4635307 7.0 0.5222681 0.3534255 0.0000000
## 1984 1.4635307 7.0 0.5222681 0.3534255 0.0000000
## 1985 1.4635307 7.0 0.5222681 0.3534255 0.0000000
## 1986 1.4635307 7.0 0.5222681 0.3534255 0.0000000
## 1987 1.4635307 7.0 0.5222681 0.3534255 0.0000000
## 1988 1.4635307 7.0 0.5222681 0.3534255 0.0000000
## 1989 1.4635307 7.0 0.5222681 0.3534255 0.0000000
## 1990 1.4635307 7.0 0.5222681 0.3534255 0.0000000
## 1991 1.4635307 7.0 0.5222681 0.3534255 0.0000000
## 1992 1.4635307 7.0 0.5222681 0.3534255 0.0000000
## 1993 1.4969088 6.0 0.6928848 0.5300870 0.0000000
## 1994 1.4969088 6.0 0.6928848 0.5300870 0.0000000
## 1995 1.4969088 6.0 0.6928848 0.5300870 0.0000000
## 1996 1.4969088 6.0 0.6928848 0.5300870 0.0000000
## 1997 1.4969088 6.0 0.6928848 0.5300870 0.0000000
## 1998 1.4969088 6.0 0.6928848 0.5300870 0.0000000
## 1999 1.4969088 6.0 0.6928848 0.5300870 0.0000000
## 2000 1.4969088 6.0 0.6928848 0.5300870 0.0000000
## 2001 1.4969088 6.0 0.6928848 0.5300870 0.0000000
## 2002 1.4969088 6.0 0.6928848 0.5300870 0.0000000
## 2003 1.4969088 6.0 0.6928848 0.5300870 0.0000000
## 2004 1.4969088 6.0 0.6928848 0.5300870 0.0000000
## 2005 1.4969088 6.0 0.6928848 0.5300870 0.0000000
## 2006 1.4969088 6.0 0.6928848 0.5300870 0.0000000
## 2007 1.4969088 6.0 0.6928848 0.5300870 0.0000000
## 2008 1.4969088 6.0 0.6928848 0.5300870 0.0000000
## 2009 1.4969088 6.0 0.6928848 0.5300870 0.0000000
## 2010 1.4969088 6.0 0.6928848 0.5300870 0.0000000
## 2011 1.4969088 6.0 0.6928848 0.5300870 0.0000000
## 2012 1.4969088 6.0 0.6928848 0.5300870 0.0000000
## 2013 1.4969088 6.0 0.6928848 0.5300870 0.0000000
## 2014 1.4969088 6.0 0.6928848 0.5300870 0.0000000
## 2015 1.4969088 6.0 0.6928848 0.5300870 0.0000000
## 2016 1.4969088 6.0 0.6928848 0.5300870 0.0000000
## 2017 1.4098276 6.0 0.6760997 0.5106878 0.2500000
## 2018 1.4098276 6.0 0.6760997 0.5106878 0.2500000
## 2019 1.4098276 6.0 0.6760997 0.5106878 0.2500000
## 2020 1.4098276 6.0 0.6760997 0.5106878 0.2500000
## 2021 1.4098276 6.0 0.6760997 0.5106878 0.2500000
## 2022 1.4098276 6.0 0.6760997 0.5106878 0.2500000
## 2023 1.4098276 6.0 0.6760997 0.5106878 0.2500000
## 2024 1.4098276 6.0 0.6760997 0.5106878 0.2500000
## 2025 1.4098276 6.0 0.6760997 0.5106878 0.2500000
## 2026 1.4098276 6.0 0.6760997 0.5106878 0.2500000
## 2027 1.4098276 6.0 0.6760997 0.5106878 0.2500000
## 2028 1.4098276 6.0 0.6760997 0.5106878 0.2500000
## 2029 1.4098276 6.0 0.6760997 0.5106878 0.2500000
## 2030 1.4098276 6.0 0.6760997 0.5106878 0.2500000
## 2031 1.4098276 6.0 0.6760997 0.5106878 0.2500000
## 2032 1.4098276 6.0 0.6760997 0.5106878 0.2500000
## 2033 1.4098276 6.0 0.6760997 0.5106878 0.2500000
## 2034 1.4098276 6.0 0.6760997 0.5106878 0.2500000
## 2035 1.4098276 6.0 0.6760997 0.5106878 0.2500000
## 2036 1.4098276 6.0 0.6760997 0.5106878 0.2500000
## 2037 1.4098276 6.0 0.6760997 0.5106878 0.2500000
## 2038 1.4098276 6.0 0.6760997 0.5106878 0.2500000
## 2039 1.4098276 6.0 0.6760997 0.5106878 0.2500000
## 2040 1.4098276 6.0 0.6760997 0.5106878 0.2500000
## 2041 0.8677885 4.0 0.8623853 0.7580645 0.5000000
## 2042 0.8677885 4.0 0.8623853 0.7580645 0.5000000
## 2043 0.8677885 4.0 0.8623853 0.7580645 0.5000000
## 2044 0.8677885 4.0 0.8623853 0.7580645 0.5000000
## 2045 0.8677885 4.0 0.8623853 0.7580645 0.5000000
## 2046 0.8677885 4.0 0.8623853 0.7580645 0.5000000
## 2047 0.8677885 4.0 0.8623853 0.7580645 0.5000000
## 2048 0.8677885 4.0 0.8623853 0.7580645 0.5000000
## 2049 0.8677885 4.0 0.8623853 0.7580645 0.5000000
## 2050 0.8677885 4.0 0.8623853 0.7580645 0.5000000
## 2051 0.8677885 4.0 0.8623853 0.7580645 0.5000000
## 2052 0.8677885 4.0 0.8623853 0.7580645 0.5000000
## 2053 0.8677885 4.0 0.8623853 0.7580645 0.5000000
## 2054 0.8677885 4.0 0.8623853 0.7580645 0.5000000
## 2055 0.8677885 4.0 0.8623853 0.7580645 0.5000000
## 2056 0.8677885 4.0 0.8623853 0.7580645 0.5000000
## 2057 0.8677885 4.0 0.8623853 0.7580645 0.5000000
## 2058 0.8677885 4.0 0.8623853 0.7580645 0.5000000
## 2059 0.8677885 4.0 0.8623853 0.7580645 0.5000000
## 2060 0.8677885 4.0 0.8623853 0.7580645 0.5000000
## 2061 0.8677885 4.0 0.8623853 0.7580645 0.5000000
## 2062 0.8677885 4.0 0.8623853 0.7580645 0.5000000
## 2063 0.8677885 4.0 0.8623853 0.7580645 0.5000000
## 2064 0.8677885 4.0 0.8623853 0.7580645 0.5000000
## 2065 1.0349319 5.0 0.8940736 0.8084387 0.0000000
## 2066 1.0349319 5.0 0.8940736 0.8084387 0.0000000
## 2067 1.0349319 5.0 0.8940736 0.8084387 0.0000000
## 2068 1.0349319 5.0 0.8940736 0.8084387 0.0000000
## 2069 1.0349319 5.0 0.8940736 0.8084387 0.0000000
## 2070 1.0349319 5.0 0.8940736 0.8084387 0.0000000
## 2071 1.0349319 5.0 0.8940736 0.8084387 0.0000000
## 2072 1.0349319 5.0 0.8940736 0.8084387 0.0000000
## 2073 1.0349319 5.0 0.8940736 0.8084387 0.0000000
## 2074 1.0349319 5.0 0.8940736 0.8084387 0.0000000
## 2075 1.0349319 5.0 0.8940736 0.8084387 0.0000000
## 2076 1.0349319 5.0 0.8940736 0.8084387 0.0000000
## 2077 1.0349319 5.0 0.8940736 0.8084387 0.0000000
## 2078 1.0349319 5.0 0.8940736 0.8084387 0.0000000
## 2079 1.0349319 5.0 0.8940736 0.8084387 0.0000000
## 2080 1.0349319 5.0 0.8940736 0.8084387 0.0000000
## 2081 1.0349319 5.0 0.8940736 0.8084387 0.0000000
## 2082 1.0349319 5.0 0.8940736 0.8084387 0.0000000
## 2083 1.0349319 5.0 0.8940736 0.8084387 0.0000000
## 2084 1.0349319 5.0 0.8940736 0.8084387 0.0000000
## 2085 1.0349319 5.0 0.8940736 0.8084387 0.0000000
## 2086 1.0349319 5.0 0.8940736 0.8084387 0.0000000
## 2087 1.0349319 5.0 0.8940736 0.8084387 0.0000000
## 2088 1.0349319 5.0 0.8940736 0.8084387 0.0000000
## 2089 0.8886913 5.5 0.8939828 0.8082902 0.5000000
## 2090 0.8886913 5.5 0.8939828 0.8082902 0.5000000
## 2091 0.8886913 5.5 0.8939828 0.8082902 0.5000000
## 2092 0.8886913 5.5 0.8939828 0.8082902 0.5000000
## 2093 0.8886913 5.5 0.8939828 0.8082902 0.5000000
## 2094 0.8886913 5.5 0.8939828 0.8082902 0.5000000
## 2095 0.8886913 5.5 0.8939828 0.8082902 0.5000000
## 2096 0.8886913 5.5 0.8939828 0.8082902 0.5000000
## 2097 0.8886913 5.5 0.8939828 0.8082902 0.5000000
## 2098 0.8886913 5.5 0.8939828 0.8082902 0.5000000
## 2099 0.8886913 5.5 0.8939828 0.8082902 0.5000000
## 2100 0.8886913 5.5 0.8939828 0.8082902 0.5000000
## 2101 0.8886913 5.5 0.8939828 0.8082902 0.5000000
## 2102 0.8886913 5.5 0.8939828 0.8082902 0.5000000
## 2103 0.8886913 5.5 0.8939828 0.8082902 0.5000000
## 2104 0.8886913 5.5 0.8939828 0.8082902 0.5000000
## 2105 0.8886913 5.5 0.8939828 0.8082902 0.5000000
## 2106 0.8886913 5.5 0.8939828 0.8082902 0.5000000
## 2107 0.8886913 5.5 0.8939828 0.8082902 0.5000000
## 2108 0.8886913 5.5 0.8939828 0.8082902 0.5000000
## 2109 0.8886913 5.5 0.8939828 0.8082902 0.5000000
## 2110 0.8886913 5.5 0.8939828 0.8082902 0.5000000
## 2111 0.8886913 5.5 0.8939828 0.8082902 0.5000000
## 2112 0.8886913 5.5 0.8939828 0.8082902 0.5000000
## 2113 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 2114 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 2115 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 2116 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 2117 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 2118 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 2119 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 2120 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 2121 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 2122 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 2123 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 2124 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 2125 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 2126 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 2127 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 2128 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 2129 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 2130 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 2131 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 2132 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 2133 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 2134 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 2135 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 2136 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 2137 1.6928054 7.5 0.5989357 0.4274862 0.1666667
## 2138 1.6928054 7.5 0.5989357 0.4274862 0.1666667
## 2139 1.6928054 7.5 0.5989357 0.4274862 0.1666667
## 2140 1.6928054 7.5 0.5989357 0.4274862 0.1666667
## 2141 1.6928054 7.5 0.5989357 0.4274862 0.1666667
## 2142 1.6928054 7.5 0.5989357 0.4274862 0.1666667
## 2143 1.6928054 7.5 0.5989357 0.4274862 0.1666667
## 2144 1.6928054 7.5 0.5989357 0.4274862 0.1666667
## 2145 1.6928054 7.5 0.5989357 0.4274862 0.1666667
## 2146 1.6928054 7.5 0.5989357 0.4274862 0.1666667
## 2147 1.6928054 7.5 0.5989357 0.4274862 0.1666667
## 2148 1.6928054 7.5 0.5989357 0.4274862 0.1666667
## 2149 1.6928054 7.5 0.5989357 0.4274862 0.1666667
## 2150 1.6928054 7.5 0.5989357 0.4274862 0.1666667
## 2151 1.6928054 7.5 0.5989357 0.4274862 0.1666667
## 2152 1.6928054 7.5 0.5989357 0.4274862 0.1666667
## 2153 1.6928054 7.5 0.5989357 0.4274862 0.1666667
## 2154 1.6928054 7.5 0.5989357 0.4274862 0.1666667
## 2155 1.6928054 7.5 0.5989357 0.4274862 0.1666667
## 2156 1.6928054 7.5 0.5989357 0.4274862 0.1666667
## 2157 1.6928054 7.5 0.5989357 0.4274862 0.1666667
## 2158 1.6928054 7.5 0.5989357 0.4274862 0.1666667
## 2159 1.6928054 7.5 0.5989357 0.4274862 0.1666667
## 2160 1.6928054 7.5 0.5989357 0.4274862 0.1666667
## 2161 1.3904874 5.5 0.2931267 0.1717331 0.0000000
## 2162 1.3904874 5.5 0.2931267 0.1717331 0.0000000
## 2163 1.3904874 5.5 0.2931267 0.1717331 0.0000000
## 2164 1.3904874 5.5 0.2931267 0.1717331 0.0000000
## 2165 1.3904874 5.5 0.2931267 0.1717331 0.0000000
## 2166 1.3904874 5.5 0.2931267 0.1717331 0.0000000
## 2167 1.3904874 5.5 0.2931267 0.1717331 0.0000000
## 2168 1.3904874 5.5 0.2931267 0.1717331 0.0000000
## 2169 1.3904874 5.5 0.2931267 0.1717331 0.0000000
## 2170 1.3904874 5.5 0.2931267 0.1717331 0.0000000
## 2171 1.3904874 5.5 0.2931267 0.1717331 0.0000000
## 2172 1.3904874 5.5 0.2931267 0.1717331 0.0000000
## 2173 1.3904874 5.5 0.2931267 0.1717331 0.0000000
## 2174 1.3904874 5.5 0.2931267 0.1717331 0.0000000
## 2175 1.3904874 5.5 0.2931267 0.1717331 0.0000000
## 2176 1.3904874 5.5 0.2931267 0.1717331 0.0000000
## 2177 1.3904874 5.5 0.2931267 0.1717331 0.0000000
## 2178 1.3904874 5.5 0.2931267 0.1717331 0.0000000
## 2179 1.3904874 5.5 0.2931267 0.1717331 0.0000000
## 2180 1.3904874 5.5 0.2931267 0.1717331 0.0000000
## 2181 1.3904874 5.5 0.2931267 0.1717331 0.0000000
## 2182 1.3904874 5.5 0.2931267 0.1717331 0.0000000
## 2183 1.3904874 5.5 0.2931267 0.1717331 0.0000000
## 2184 1.3904874 5.5 0.2931267 0.1717331 0.0000000
## 2185 1.4161738 6.0 0.5734298 0.4019640 0.0000000
## 2186 1.4161738 6.0 0.5734298 0.4019640 0.0000000
## 2187 1.4161738 6.0 0.5734298 0.4019640 0.0000000
## 2188 1.4161738 6.0 0.5734298 0.4019640 0.0000000
## 2189 1.4161738 6.0 0.5734298 0.4019640 0.0000000
## 2190 1.4161738 6.0 0.5734298 0.4019640 0.0000000
## 2191 1.4161738 6.0 0.5734298 0.4019640 0.0000000
## 2192 1.4161738 6.0 0.5734298 0.4019640 0.0000000
## 2193 1.4161738 6.0 0.5734298 0.4019640 0.0000000
## 2194 1.4161738 6.0 0.5734298 0.4019640 0.0000000
## 2195 1.4161738 6.0 0.5734298 0.4019640 0.0000000
## 2196 1.4161738 6.0 0.5734298 0.4019640 0.0000000
## 2197 1.4161738 6.0 0.5734298 0.4019640 0.0000000
## 2198 1.4161738 6.0 0.5734298 0.4019640 0.0000000
## 2199 1.4161738 6.0 0.5734298 0.4019640 0.0000000
## 2200 1.4161738 6.0 0.5734298 0.4019640 0.0000000
## 2201 1.4161738 6.0 0.5734298 0.4019640 0.0000000
## 2202 1.4161738 6.0 0.5734298 0.4019640 0.0000000
## 2203 1.4161738 6.0 0.5734298 0.4019640 0.0000000
## 2204 1.4161738 6.0 0.5734298 0.4019640 0.0000000
## 2205 1.4161738 6.0 0.5734298 0.4019640 0.0000000
## 2206 1.4161738 6.0 0.5734298 0.4019640 0.0000000
## 2207 1.4161738 6.0 0.5734298 0.4019640 0.0000000
## 2208 1.4161738 6.0 0.5734298 0.4019640 0.0000000
## 2209 1.1830432 4.5 0.5461783 0.3756846 0.2500000
## 2210 1.1830432 4.5 0.5461783 0.3756846 0.2500000
## 2211 1.1830432 4.5 0.5461783 0.3756846 0.2500000
## 2212 1.1830432 4.5 0.5461783 0.3756846 0.2500000
## 2213 1.1830432 4.5 0.5461783 0.3756846 0.2500000
## 2214 1.1830432 4.5 0.5461783 0.3756846 0.2500000
## 2215 1.1830432 4.5 0.5461783 0.3756846 0.2500000
## 2216 1.1830432 4.5 0.5461783 0.3756846 0.2500000
## 2217 1.1830432 4.5 0.5461783 0.3756846 0.2500000
## 2218 1.1830432 4.5 0.5461783 0.3756846 0.2500000
## 2219 1.1830432 4.5 0.5461783 0.3756846 0.2500000
## 2220 1.1830432 4.5 0.5461783 0.3756846 0.2500000
## 2221 1.1830432 4.5 0.5461783 0.3756846 0.2500000
## 2222 1.1830432 4.5 0.5461783 0.3756846 0.2500000
## 2223 1.1830432 4.5 0.5461783 0.3756846 0.2500000
## 2224 1.1830432 4.5 0.5461783 0.3756846 0.2500000
## 2225 1.1830432 4.5 0.5461783 0.3756846 0.2500000
## 2226 1.1830432 4.5 0.5461783 0.3756846 0.2500000
## 2227 1.1830432 4.5 0.5461783 0.3756846 0.2500000
## 2228 1.1830432 4.5 0.5461783 0.3756846 0.2500000
## 2229 1.1830432 4.5 0.5461783 0.3756846 0.2500000
## 2230 1.1830432 4.5 0.5461783 0.3756846 0.2500000
## 2231 1.1830432 4.5 0.5461783 0.3756846 0.2500000
## 2232 1.1830432 4.5 0.5461783 0.3756846 0.2500000
## 2233 1.0779045 5.0 0.8424721 0.7278202 0.0000000
## 2234 1.0779045 5.0 0.8424721 0.7278202 0.0000000
## 2235 1.0779045 5.0 0.8424721 0.7278202 0.0000000
## 2236 1.0779045 5.0 0.8424721 0.7278202 0.0000000
## 2237 1.0779045 5.0 0.8424721 0.7278202 0.0000000
## 2238 1.0779045 5.0 0.8424721 0.7278202 0.0000000
## 2239 1.0779045 5.0 0.8424721 0.7278202 0.0000000
## 2240 1.0779045 5.0 0.8424721 0.7278202 0.0000000
## 2241 1.0779045 5.0 0.8424721 0.7278202 0.0000000
## 2242 1.0779045 5.0 0.8424721 0.7278202 0.0000000
## 2243 1.0779045 5.0 0.8424721 0.7278202 0.0000000
## 2244 1.0779045 5.0 0.8424721 0.7278202 0.0000000
## 2245 1.0779045 5.0 0.8424721 0.7278202 0.0000000
## 2246 1.0779045 5.0 0.8424721 0.7278202 0.0000000
## 2247 1.0779045 5.0 0.8424721 0.7278202 0.0000000
## 2248 1.0779045 5.0 0.8424721 0.7278202 0.0000000
## 2249 1.0779045 5.0 0.8424721 0.7278202 0.0000000
## 2250 1.0779045 5.0 0.8424721 0.7278202 0.0000000
## 2251 1.0779045 5.0 0.8424721 0.7278202 0.0000000
## 2252 1.0779045 5.0 0.8424721 0.7278202 0.0000000
## 2253 1.0779045 5.0 0.8424721 0.7278202 0.0000000
## 2254 1.0779045 5.0 0.8424721 0.7278202 0.0000000
## 2255 1.0779045 5.0 0.8424721 0.7278202 0.0000000
## 2256 1.0779045 5.0 0.8424721 0.7278202 0.0000000
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## 2258 1.1343318 5.0 0.9128705 0.8397072 0.0000000
## 2259 1.1343318 5.0 0.9128705 0.8397072 0.0000000
## 2260 1.1343318 5.0 0.9128705 0.8397072 0.0000000
## 2261 1.1343318 5.0 0.9128705 0.8397072 0.0000000
## 2262 1.1343318 5.0 0.9128705 0.8397072 0.0000000
## 2263 1.1343318 5.0 0.9128705 0.8397072 0.0000000
## 2264 1.1343318 5.0 0.9128705 0.8397072 0.0000000
## 2265 1.1343318 5.0 0.9128705 0.8397072 0.0000000
## 2266 1.1343318 5.0 0.9128705 0.8397072 0.0000000
## 2267 1.1343318 5.0 0.9128705 0.8397072 0.0000000
## 2268 1.1343318 5.0 0.9128705 0.8397072 0.0000000
## 2269 1.1343318 5.0 0.9128705 0.8397072 0.0000000
## 2270 1.1343318 5.0 0.9128705 0.8397072 0.0000000
## 2271 1.1343318 5.0 0.9128705 0.8397072 0.0000000
## 2272 1.1343318 5.0 0.9128705 0.8397072 0.0000000
## 2273 1.1343318 5.0 0.9128705 0.8397072 0.0000000
## 2274 1.1343318 5.0 0.9128705 0.8397072 0.0000000
## 2275 1.1343318 5.0 0.9128705 0.8397072 0.0000000
## 2276 1.1343318 5.0 0.9128705 0.8397072 0.0000000
## 2277 1.1343318 5.0 0.9128705 0.8397072 0.0000000
## 2278 1.1343318 5.0 0.9128705 0.8397072 0.0000000
## 2279 1.1343318 5.0 0.9128705 0.8397072 0.0000000
## 2280 1.1343318 5.0 0.9128705 0.8397072 0.0000000
## 2281 0.9541375 4.5 0.8634686 0.7597403 0.5000000
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## 2283 0.9541375 4.5 0.8634686 0.7597403 0.5000000
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## 2285 0.9541375 4.5 0.8634686 0.7597403 0.5000000
## 2286 0.9541375 4.5 0.8634686 0.7597403 0.5000000
## 2287 0.9541375 4.5 0.8634686 0.7597403 0.5000000
## 2288 0.9541375 4.5 0.8634686 0.7597403 0.5000000
## 2289 0.9541375 4.5 0.8634686 0.7597403 0.5000000
## 2290 0.9541375 4.5 0.8634686 0.7597403 0.5000000
## 2291 0.9541375 4.5 0.8634686 0.7597403 0.5000000
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## 2294 0.9541375 4.5 0.8634686 0.7597403 0.5000000
## 2295 0.9541375 4.5 0.8634686 0.7597403 0.5000000
## 2296 0.9541375 4.5 0.8634686 0.7597403 0.5000000
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## 2298 0.9541375 4.5 0.8634686 0.7597403 0.5000000
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## 2301 0.9541375 4.5 0.8634686 0.7597403 0.5000000
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## 2303 0.9541375 4.5 0.8634686 0.7597403 0.5000000
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## 2306 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 2307 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 2308 2.0784670 11.0 0.0000000 0.0000000 0.0000000
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## 2312 2.0784670 11.0 0.0000000 0.0000000 0.0000000
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## 2318 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 2319 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 2320 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 2321 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 2322 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 2323 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 2324 2.0784670 11.0 0.0000000 0.0000000 0.0000000
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## 2326 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 2327 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 2328 2.0784670 11.0 0.0000000 0.0000000 0.0000000
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## 2330 1.5937474 7.5 0.3858672 0.2390555 0.1666667
## 2331 1.5937474 7.5 0.3858672 0.2390555 0.1666667
## 2332 1.5937474 7.5 0.3858672 0.2390555 0.1666667
## 2333 1.5937474 7.5 0.3858672 0.2390555 0.1666667
## 2334 1.5937474 7.5 0.3858672 0.2390555 0.1666667
## 2335 1.5937474 7.5 0.3858672 0.2390555 0.1666667
## 2336 1.5937474 7.5 0.3858672 0.2390555 0.1666667
## 2337 1.5937474 7.5 0.3858672 0.2390555 0.1666667
## 2338 1.5937474 7.5 0.3858672 0.2390555 0.1666667
## 2339 1.5937474 7.5 0.3858672 0.2390555 0.1666667
## 2340 1.5937474 7.5 0.3858672 0.2390555 0.1666667
## 2341 1.5937474 7.5 0.3858672 0.2390555 0.1666667
## 2342 1.5937474 7.5 0.3858672 0.2390555 0.1666667
## 2343 1.5937474 7.5 0.3858672 0.2390555 0.1666667
## 2344 1.5937474 7.5 0.3858672 0.2390555 0.1666667
## 2345 1.5937474 7.5 0.3858672 0.2390555 0.1666667
## 2346 1.5937474 7.5 0.3858672 0.2390555 0.1666667
## 2347 1.5937474 7.5 0.3858672 0.2390555 0.1666667
## 2348 1.5937474 7.5 0.3858672 0.2390555 0.1666667
## 2349 1.5937474 7.5 0.3858672 0.2390555 0.1666667
## 2350 1.5937474 7.5 0.3858672 0.2390555 0.1666667
## 2351 1.5937474 7.5 0.3858672 0.2390555 0.1666667
## 2352 1.5937474 7.5 0.3858672 0.2390555 0.1666667
## 2353 1.6035169 7.5 0.3980177 0.2484533 0.0000000
## 2354 1.6035169 7.5 0.3980177 0.2484533 0.0000000
## 2355 1.6035169 7.5 0.3980177 0.2484533 0.0000000
## 2356 1.6035169 7.5 0.3980177 0.2484533 0.0000000
## 2357 1.6035169 7.5 0.3980177 0.2484533 0.0000000
## 2358 1.6035169 7.5 0.3980177 0.2484533 0.0000000
## 2359 1.6035169 7.5 0.3980177 0.2484533 0.0000000
## 2360 1.6035169 7.5 0.3980177 0.2484533 0.0000000
## 2361 1.6035169 7.5 0.3980177 0.2484533 0.0000000
## 2362 1.6035169 7.5 0.3980177 0.2484533 0.0000000
## 2363 1.6035169 7.5 0.3980177 0.2484533 0.0000000
## 2364 1.6035169 7.5 0.3980177 0.2484533 0.0000000
## 2365 1.6035169 7.5 0.3980177 0.2484533 0.0000000
## 2366 1.6035169 7.5 0.3980177 0.2484533 0.0000000
## 2367 1.6035169 7.5 0.3980177 0.2484533 0.0000000
## 2368 1.6035169 7.5 0.3980177 0.2484533 0.0000000
## 2369 1.6035169 7.5 0.3980177 0.2484533 0.0000000
## 2370 1.6035169 7.5 0.3980177 0.2484533 0.0000000
## 2371 1.6035169 7.5 0.3980177 0.2484533 0.0000000
## 2372 1.6035169 7.5 0.3980177 0.2484533 0.0000000
## 2373 1.6035169 7.5 0.3980177 0.2484533 0.0000000
## 2374 1.6035169 7.5 0.3980177 0.2484533 0.0000000
## 2375 1.6035169 7.5 0.3980177 0.2484533 0.0000000
## 2376 1.6035169 7.5 0.3980177 0.2484533 0.0000000
## 2377 1.5172828 6.5 0.6578032 0.4900944 0.0000000
## 2378 1.5172828 6.5 0.6578032 0.4900944 0.0000000
## 2379 1.5172828 6.5 0.6578032 0.4900944 0.0000000
## 2380 1.5172828 6.5 0.6578032 0.4900944 0.0000000
## 2381 1.5172828 6.5 0.6578032 0.4900944 0.0000000
## 2382 1.5172828 6.5 0.6578032 0.4900944 0.0000000
## 2383 1.5172828 6.5 0.6578032 0.4900944 0.0000000
## 2384 1.5172828 6.5 0.6578032 0.4900944 0.0000000
## 2385 1.5172828 6.5 0.6578032 0.4900944 0.0000000
## 2386 1.5172828 6.5 0.6578032 0.4900944 0.0000000
## 2387 1.5172828 6.5 0.6578032 0.4900944 0.0000000
## 2388 1.5172828 6.5 0.6578032 0.4900944 0.0000000
## 2389 1.5172828 6.5 0.6578032 0.4900944 0.0000000
## 2390 1.5172828 6.5 0.6578032 0.4900944 0.0000000
## 2391 1.5172828 6.5 0.6578032 0.4900944 0.0000000
## 2392 1.5172828 6.5 0.6578032 0.4900944 0.0000000
## 2393 1.5172828 6.5 0.6578032 0.4900944 0.0000000
## 2394 1.5172828 6.5 0.6578032 0.4900944 0.0000000
## 2395 1.5172828 6.5 0.6578032 0.4900944 0.0000000
## 2396 1.5172828 6.5 0.6578032 0.4900944 0.0000000
## 2397 1.5172828 6.5 0.6578032 0.4900944 0.0000000
## 2398 1.5172828 6.5 0.6578032 0.4900944 0.0000000
## 2399 1.5172828 6.5 0.6578032 0.4900944 0.0000000
## 2400 1.5172828 6.5 0.6578032 0.4900944 0.0000000
## 2401 1.3203870 5.5 0.5871069 0.4155353 0.2500000
## 2402 1.3203870 5.5 0.5871069 0.4155353 0.2500000
## 2403 1.3203870 5.5 0.5871069 0.4155353 0.2500000
## 2404 1.3203870 5.5 0.5871069 0.4155353 0.2500000
## 2405 1.3203870 5.5 0.5871069 0.4155353 0.2500000
## 2406 1.3203870 5.5 0.5871069 0.4155353 0.2500000
## 2407 1.3203870 5.5 0.5871069 0.4155353 0.2500000
## 2408 1.3203870 5.5 0.5871069 0.4155353 0.2500000
## 2409 1.3203870 5.5 0.5871069 0.4155353 0.2500000
## 2410 1.3203870 5.5 0.5871069 0.4155353 0.2500000
## 2411 1.3203870 5.5 0.5871069 0.4155353 0.2500000
## 2412 1.3203870 5.5 0.5871069 0.4155353 0.2500000
## 2413 1.3203870 5.5 0.5871069 0.4155353 0.2500000
## 2414 1.3203870 5.5 0.5871069 0.4155353 0.2500000
## 2415 1.3203870 5.5 0.5871069 0.4155353 0.2500000
## 2416 1.3203870 5.5 0.5871069 0.4155353 0.2500000
## 2417 1.3203870 5.5 0.5871069 0.4155353 0.2500000
## 2418 1.3203870 5.5 0.5871069 0.4155353 0.2500000
## 2419 1.3203870 5.5 0.5871069 0.4155353 0.2500000
## 2420 1.3203870 5.5 0.5871069 0.4155353 0.2500000
## 2421 1.3203870 5.5 0.5871069 0.4155353 0.2500000
## 2422 1.3203870 5.5 0.5871069 0.4155353 0.2500000
## 2423 1.3203870 5.5 0.5871069 0.4155353 0.2500000
## 2424 1.3203870 5.5 0.5871069 0.4155353 0.2500000
## 2425 0.9457610 4.0 0.8813489 0.7878675 0.5000000
## 2426 0.9457610 4.0 0.8813489 0.7878675 0.5000000
## 2427 0.9457610 4.0 0.8813489 0.7878675 0.5000000
## 2428 0.9457610 4.0 0.8813489 0.7878675 0.5000000
## 2429 0.9457610 4.0 0.8813489 0.7878675 0.5000000
## 2430 0.9457610 4.0 0.8813489 0.7878675 0.5000000
## 2431 0.9457610 4.0 0.8813489 0.7878675 0.5000000
## 2432 0.9457610 4.0 0.8813489 0.7878675 0.5000000
## 2433 0.9457610 4.0 0.8813489 0.7878675 0.5000000
## 2434 0.9457610 4.0 0.8813489 0.7878675 0.5000000
## 2435 0.9457610 4.0 0.8813489 0.7878675 0.5000000
## 2436 0.9457610 4.0 0.8813489 0.7878675 0.5000000
## 2437 0.9457610 4.0 0.8813489 0.7878675 0.5000000
## 2438 0.9457610 4.0 0.8813489 0.7878675 0.5000000
## 2439 0.9457610 4.0 0.8813489 0.7878675 0.5000000
## 2440 0.9457610 4.0 0.8813489 0.7878675 0.5000000
## 2441 0.9457610 4.0 0.8813489 0.7878675 0.5000000
## 2442 0.9457610 4.0 0.8813489 0.7878675 0.5000000
## 2443 0.9457610 4.0 0.8813489 0.7878675 0.5000000
## 2444 0.9457610 4.0 0.8813489 0.7878675 0.5000000
## 2445 0.9457610 4.0 0.8813489 0.7878675 0.5000000
## 2446 0.9457610 4.0 0.8813489 0.7878675 0.5000000
## 2447 0.9457610 4.0 0.8813489 0.7878675 0.5000000
## 2448 0.9457610 4.0 0.8813489 0.7878675 0.5000000
## 2449 1.0476403 5.5 0.8832972 0.7909868 0.0000000
## 2450 1.0476403 5.5 0.8832972 0.7909868 0.0000000
## 2451 1.0476403 5.5 0.8832972 0.7909868 0.0000000
## 2452 1.0476403 5.5 0.8832972 0.7909868 0.0000000
## 2453 1.0476403 5.5 0.8832972 0.7909868 0.0000000
## 2454 1.0476403 5.5 0.8832972 0.7909868 0.0000000
## 2455 1.0476403 5.5 0.8832972 0.7909868 0.0000000
## 2456 1.0476403 5.5 0.8832972 0.7909868 0.0000000
## 2457 1.0476403 5.5 0.8832972 0.7909868 0.0000000
## 2458 1.0476403 5.5 0.8832972 0.7909868 0.0000000
## 2459 1.0476403 5.5 0.8832972 0.7909868 0.0000000
## 2460 1.0476403 5.5 0.8832972 0.7909868 0.0000000
## 2461 1.0476403 5.5 0.8832972 0.7909868 0.0000000
## 2462 1.0476403 5.5 0.8832972 0.7909868 0.0000000
## 2463 1.0476403 5.5 0.8832972 0.7909868 0.0000000
## 2464 1.0476403 5.5 0.8832972 0.7909868 0.0000000
## 2465 1.0476403 5.5 0.8832972 0.7909868 0.0000000
## 2466 1.0476403 5.5 0.8832972 0.7909868 0.0000000
## 2467 1.0476403 5.5 0.8832972 0.7909868 0.0000000
## 2468 1.0476403 5.5 0.8832972 0.7909868 0.0000000
## 2469 1.0476403 5.5 0.8832972 0.7909868 0.0000000
## 2470 1.0476403 5.5 0.8832972 0.7909868 0.0000000
## 2471 1.0476403 5.5 0.8832972 0.7909868 0.0000000
## 2472 1.0476403 5.5 0.8832972 0.7909868 0.0000000
## 2473 0.8264735 4.5 0.9161354 0.8452490 0.5000000
## 2474 0.8264735 4.5 0.9161354 0.8452490 0.5000000
## 2475 0.8264735 4.5 0.9161354 0.8452490 0.5000000
## 2476 0.8264735 4.5 0.9161354 0.8452490 0.5000000
## 2477 0.8264735 4.5 0.9161354 0.8452490 0.5000000
## 2478 0.8264735 4.5 0.9161354 0.8452490 0.5000000
## 2479 0.8264735 4.5 0.9161354 0.8452490 0.5000000
## 2480 0.8264735 4.5 0.9161354 0.8452490 0.5000000
## 2481 0.8264735 4.5 0.9161354 0.8452490 0.5000000
## 2482 0.8264735 4.5 0.9161354 0.8452490 0.5000000
## 2483 0.8264735 4.5 0.9161354 0.8452490 0.5000000
## 2484 0.8264735 4.5 0.9161354 0.8452490 0.5000000
## 2485 0.8264735 4.5 0.9161354 0.8452490 0.5000000
## 2486 0.8264735 4.5 0.9161354 0.8452490 0.5000000
## 2487 0.8264735 4.5 0.9161354 0.8452490 0.5000000
## 2488 0.8264735 4.5 0.9161354 0.8452490 0.5000000
## 2489 0.8264735 4.5 0.9161354 0.8452490 0.5000000
## 2490 0.8264735 4.5 0.9161354 0.8452490 0.5000000
## 2491 0.8264735 4.5 0.9161354 0.8452490 0.5000000
## 2492 0.8264735 4.5 0.9161354 0.8452490 0.5000000
## 2493 0.8264735 4.5 0.9161354 0.8452490 0.5000000
## 2494 0.8264735 4.5 0.9161354 0.8452490 0.5000000
## 2495 0.8264735 4.5 0.9161354 0.8452490 0.5000000
## 2496 0.8264735 4.5 0.9161354 0.8452490 0.5000000
## 2497 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 2498 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 2499 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 2500 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 2501 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 2502 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 2503 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 2504 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 2505 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 2506 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 2507 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 2508 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 2509 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 2510 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 2511 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 2512 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 2513 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 2514 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 2515 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 2516 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 2517 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 2518 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 2519 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 2520 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 2521 1.4908950 6.5 0.4258033 0.2704892 0.0000000
## 2522 1.4908950 6.5 0.4258033 0.2704892 0.0000000
## 2523 1.4908950 6.5 0.4258033 0.2704892 0.0000000
## 2524 1.4908950 6.5 0.4258033 0.2704892 0.0000000
## 2525 1.4908950 6.5 0.4258033 0.2704892 0.0000000
## 2526 1.4908950 6.5 0.4258033 0.2704892 0.0000000
## 2527 1.4908950 6.5 0.4258033 0.2704892 0.0000000
## 2528 1.4908950 6.5 0.4258033 0.2704892 0.0000000
## 2529 1.4908950 6.5 0.4258033 0.2704892 0.0000000
## 2530 1.4908950 6.5 0.4258033 0.2704892 0.0000000
## 2531 1.4908950 6.5 0.4258033 0.2704892 0.0000000
## 2532 1.4908950 6.5 0.4258033 0.2704892 0.0000000
## 2533 1.4908950 6.5 0.4258033 0.2704892 0.0000000
## 2534 1.4908950 6.5 0.4258033 0.2704892 0.0000000
## 2535 1.4908950 6.5 0.4258033 0.2704892 0.0000000
## 2536 1.4908950 6.5 0.4258033 0.2704892 0.0000000
## 2537 1.4908950 6.5 0.4258033 0.2704892 0.0000000
## 2538 1.4908950 6.5 0.4258033 0.2704892 0.0000000
## 2539 1.4908950 6.5 0.4258033 0.2704892 0.0000000
## 2540 1.4908950 6.5 0.4258033 0.2704892 0.0000000
## 2541 1.4908950 6.5 0.4258033 0.2704892 0.0000000
## 2542 1.4908950 6.5 0.4258033 0.2704892 0.0000000
## 2543 1.4908950 6.5 0.4258033 0.2704892 0.0000000
## 2544 1.4908950 6.5 0.4258033 0.2704892 0.0000000
## 2545 1.4369842 7.0 0.5152690 0.3470454 0.0000000
## 2546 1.4369842 7.0 0.5152690 0.3470454 0.0000000
## 2547 1.4369842 7.0 0.5152690 0.3470454 0.0000000
## 2548 1.4369842 7.0 0.5152690 0.3470454 0.0000000
## 2549 1.4369842 7.0 0.5152690 0.3470454 0.0000000
## 2550 1.4369842 7.0 0.5152690 0.3470454 0.0000000
## 2551 1.4369842 7.0 0.5152690 0.3470454 0.0000000
## 2552 1.4369842 7.0 0.5152690 0.3470454 0.0000000
## 2553 1.4369842 7.0 0.5152690 0.3470454 0.0000000
## 2554 1.4369842 7.0 0.5152690 0.3470454 0.0000000
## 2555 1.4369842 7.0 0.5152690 0.3470454 0.0000000
## 2556 1.4369842 7.0 0.5152690 0.3470454 0.0000000
## 2557 1.4369842 7.0 0.5152690 0.3470454 0.0000000
## 2558 1.4369842 7.0 0.5152690 0.3470454 0.0000000
## 2559 1.4369842 7.0 0.5152690 0.3470454 0.0000000
## 2560 1.4369842 7.0 0.5152690 0.3470454 0.0000000
## 2561 1.4369842 7.0 0.5152690 0.3470454 0.0000000
## 2562 1.4369842 7.0 0.5152690 0.3470454 0.0000000
## 2563 1.4369842 7.0 0.5152690 0.3470454 0.0000000
## 2564 1.4369842 7.0 0.5152690 0.3470454 0.0000000
## 2565 1.4369842 7.0 0.5152690 0.3470454 0.0000000
## 2566 1.4369842 7.0 0.5152690 0.3470454 0.0000000
## 2567 1.4369842 7.0 0.5152690 0.3470454 0.0000000
## 2568 1.4369842 7.0 0.5152690 0.3470454 0.0000000
## 2569 1.1590750 5.0 0.3094595 0.1830536 0.2500000
## 2570 1.1590750 5.0 0.3094595 0.1830536 0.2500000
## 2571 1.1590750 5.0 0.3094595 0.1830536 0.2500000
## 2572 1.1590750 5.0 0.3094595 0.1830536 0.2500000
## 2573 1.1590750 5.0 0.3094595 0.1830536 0.2500000
## 2574 1.1590750 5.0 0.3094595 0.1830536 0.2500000
## 2575 1.1590750 5.0 0.3094595 0.1830536 0.2500000
## 2576 1.1590750 5.0 0.3094595 0.1830536 0.2500000
## 2577 1.1590750 5.0 0.3094595 0.1830536 0.2500000
## 2578 1.1590750 5.0 0.3094595 0.1830536 0.2500000
## 2579 1.1590750 5.0 0.3094595 0.1830536 0.2500000
## 2580 1.1590750 5.0 0.3094595 0.1830536 0.2500000
## 2581 1.1590750 5.0 0.3094595 0.1830536 0.2500000
## 2582 1.1590750 5.0 0.3094595 0.1830536 0.2500000
## 2583 1.1590750 5.0 0.3094595 0.1830536 0.2500000
## 2584 1.1590750 5.0 0.3094595 0.1830536 0.2500000
## 2585 1.1590750 5.0 0.3094595 0.1830536 0.2500000
## 2586 1.1590750 5.0 0.3094595 0.1830536 0.2500000
## 2587 1.1590750 5.0 0.3094595 0.1830536 0.2500000
## 2588 1.1590750 5.0 0.3094595 0.1830536 0.2500000
## 2589 1.1590750 5.0 0.3094595 0.1830536 0.2500000
## 2590 1.1590750 5.0 0.3094595 0.1830536 0.2500000
## 2591 1.1590750 5.0 0.3094595 0.1830536 0.2500000
## 2592 1.1590750 5.0 0.3094595 0.1830536 0.2500000
## 2593 1.1787010 5.0 0.5881557 0.4165868 0.2500000
## 2594 1.1787010 5.0 0.5881557 0.4165868 0.2500000
## 2595 1.1787010 5.0 0.5881557 0.4165868 0.2500000
## 2596 1.1787010 5.0 0.5881557 0.4165868 0.2500000
## 2597 1.1787010 5.0 0.5881557 0.4165868 0.2500000
## 2598 1.1787010 5.0 0.5881557 0.4165868 0.2500000
## 2599 1.1787010 5.0 0.5881557 0.4165868 0.2500000
## 2600 1.1787010 5.0 0.5881557 0.4165868 0.2500000
## 2601 1.1787010 5.0 0.5881557 0.4165868 0.2500000
## 2602 1.1787010 5.0 0.5881557 0.4165868 0.2500000
## 2603 1.1787010 5.0 0.5881557 0.4165868 0.2500000
## 2604 1.1787010 5.0 0.5881557 0.4165868 0.2500000
## 2605 1.1787010 5.0 0.5881557 0.4165868 0.2500000
## 2606 1.1787010 5.0 0.5881557 0.4165868 0.2500000
## 2607 1.1787010 5.0 0.5881557 0.4165868 0.2500000
## 2608 1.1787010 5.0 0.5881557 0.4165868 0.2500000
## 2609 1.1787010 5.0 0.5881557 0.4165868 0.2500000
## 2610 1.1787010 5.0 0.5881557 0.4165868 0.2500000
## 2611 1.1787010 5.0 0.5881557 0.4165868 0.2500000
## 2612 1.1787010 5.0 0.5881557 0.4165868 0.2500000
## 2613 1.1787010 5.0 0.5881557 0.4165868 0.2500000
## 2614 1.1787010 5.0 0.5881557 0.4165868 0.2500000
## 2615 1.1787010 5.0 0.5881557 0.4165868 0.2500000
## 2616 1.1787010 5.0 0.5881557 0.4165868 0.2500000
## 2617 0.9245130 3.5 0.8128693 0.6847345 0.5000000
## 2618 0.9245130 3.5 0.8128693 0.6847345 0.5000000
## 2619 0.9245130 3.5 0.8128693 0.6847345 0.5000000
## 2620 0.9245130 3.5 0.8128693 0.6847345 0.5000000
## 2621 0.9245130 3.5 0.8128693 0.6847345 0.5000000
## 2622 0.9245130 3.5 0.8128693 0.6847345 0.5000000
## 2623 0.9245130 3.5 0.8128693 0.6847345 0.5000000
## 2624 0.9245130 3.5 0.8128693 0.6847345 0.5000000
## 2625 0.9245130 3.5 0.8128693 0.6847345 0.5000000
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## 2628 0.9245130 3.5 0.8128693 0.6847345 0.5000000
## 2629 0.9245130 3.5 0.8128693 0.6847345 0.5000000
## 2630 0.9245130 3.5 0.8128693 0.6847345 0.5000000
## 2631 0.9245130 3.5 0.8128693 0.6847345 0.5000000
## 2632 0.9245130 3.5 0.8128693 0.6847345 0.5000000
## 2633 0.9245130 3.5 0.8128693 0.6847345 0.5000000
## 2634 0.9245130 3.5 0.8128693 0.6847345 0.5000000
## 2635 0.9245130 3.5 0.8128693 0.6847345 0.5000000
## 2636 0.9245130 3.5 0.8128693 0.6847345 0.5000000
## 2637 0.9245130 3.5 0.8128693 0.6847345 0.5000000
## 2638 0.9245130 3.5 0.8128693 0.6847345 0.5000000
## 2639 0.9245130 3.5 0.8128693 0.6847345 0.5000000
## 2640 0.9245130 3.5 0.8128693 0.6847345 0.5000000
## 2641 1.2497340 4.5 0.8109840 0.6820631 0.3333333
## 2642 1.2497340 4.5 0.8109840 0.6820631 0.3333333
## 2643 1.2497340 4.5 0.8109840 0.6820631 0.3333333
## 2644 1.2497340 4.5 0.8109840 0.6820631 0.3333333
## 2645 1.2497340 4.5 0.8109840 0.6820631 0.3333333
## 2646 1.2497340 4.5 0.8109840 0.6820631 0.3333333
## 2647 1.2497340 4.5 0.8109840 0.6820631 0.3333333
## 2648 1.2497340 4.5 0.8109840 0.6820631 0.3333333
## 2649 1.2497340 4.5 0.8109840 0.6820631 0.3333333
## 2650 1.2497340 4.5 0.8109840 0.6820631 0.3333333
## 2651 1.2497340 4.5 0.8109840 0.6820631 0.3333333
## 2652 1.2497340 4.5 0.8109840 0.6820631 0.3333333
## 2653 1.2497340 4.5 0.8109840 0.6820631 0.3333333
## 2654 1.2497340 4.5 0.8109840 0.6820631 0.3333333
## 2655 1.2497340 4.5 0.8109840 0.6820631 0.3333333
## 2656 1.2497340 4.5 0.8109840 0.6820631 0.3333333
## 2657 1.2497340 4.5 0.8109840 0.6820631 0.3333333
## 2658 1.2497340 4.5 0.8109840 0.6820631 0.3333333
## 2659 1.2497340 4.5 0.8109840 0.6820631 0.3333333
## 2660 1.2497340 4.5 0.8109840 0.6820631 0.3333333
## 2661 1.2497340 4.5 0.8109840 0.6820631 0.3333333
## 2662 1.2497340 4.5 0.8109840 0.6820631 0.3333333
## 2663 1.2497340 4.5 0.8109840 0.6820631 0.3333333
## 2664 1.2497340 4.5 0.8109840 0.6820631 0.3333333
## 2665 0.8774006 3.5 0.7224848 0.5655391 0.5000000
## 2666 0.8774006 3.5 0.7224848 0.5655391 0.5000000
## 2667 0.8774006 3.5 0.7224848 0.5655391 0.5000000
## 2668 0.8774006 3.5 0.7224848 0.5655391 0.5000000
## 2669 0.8774006 3.5 0.7224848 0.5655391 0.5000000
## 2670 0.8774006 3.5 0.7224848 0.5655391 0.5000000
## 2671 0.8774006 3.5 0.7224848 0.5655391 0.5000000
## 2672 0.8774006 3.5 0.7224848 0.5655391 0.5000000
## 2673 0.8774006 3.5 0.7224848 0.5655391 0.5000000
## 2674 0.8774006 3.5 0.7224848 0.5655391 0.5000000
## 2675 0.8774006 3.5 0.7224848 0.5655391 0.5000000
## 2676 0.8774006 3.5 0.7224848 0.5655391 0.5000000
## 2677 0.8774006 3.5 0.7224848 0.5655391 0.5000000
## 2678 0.8774006 3.5 0.7224848 0.5655391 0.5000000
## 2679 0.8774006 3.5 0.7224848 0.5655391 0.5000000
## 2680 0.8774006 3.5 0.7224848 0.5655391 0.5000000
## 2681 0.8774006 3.5 0.7224848 0.5655391 0.5000000
## 2682 0.8774006 3.5 0.7224848 0.5655391 0.5000000
## 2683 0.8774006 3.5 0.7224848 0.5655391 0.5000000
## 2684 0.8774006 3.5 0.7224848 0.5655391 0.5000000
## 2685 0.8774006 3.5 0.7224848 0.5655391 0.5000000
## 2686 0.8774006 3.5 0.7224848 0.5655391 0.5000000
## 2687 0.8774006 3.5 0.7224848 0.5655391 0.5000000
## 2688 0.8774006 3.5 0.7224848 0.5655391 0.5000000
## 2689 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 2690 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 2691 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 2692 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 2693 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 2694 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 2695 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 2696 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 2697 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 2698 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 2699 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 2700 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 2701 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 2702 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 2703 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 2704 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 2705 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 2706 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 2707 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 2708 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 2709 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 2710 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 2711 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 2712 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 2713 1.7159561 7.5 0.6949506 0.5325091 0.0000000
## 2714 1.7159561 7.5 0.6949506 0.5325091 0.0000000
## 2715 1.7159561 7.5 0.6949506 0.5325091 0.0000000
## 2716 1.7159561 7.5 0.6949506 0.5325091 0.0000000
## 2717 1.7159561 7.5 0.6949506 0.5325091 0.0000000
## 2718 1.7159561 7.5 0.6949506 0.5325091 0.0000000
## 2719 1.7159561 7.5 0.6949506 0.5325091 0.0000000
## 2720 1.7159561 7.5 0.6949506 0.5325091 0.0000000
## 2721 1.7159561 7.5 0.6949506 0.5325091 0.0000000
## 2722 1.7159561 7.5 0.6949506 0.5325091 0.0000000
## 2723 1.7159561 7.5 0.6949506 0.5325091 0.0000000
## 2724 1.7159561 7.5 0.6949506 0.5325091 0.0000000
## 2725 1.7159561 7.5 0.6949506 0.5325091 0.0000000
## 2726 1.7159561 7.5 0.6949506 0.5325091 0.0000000
## 2727 1.7159561 7.5 0.6949506 0.5325091 0.0000000
## 2728 1.7159561 7.5 0.6949506 0.5325091 0.0000000
## 2729 1.7159561 7.5 0.6949506 0.5325091 0.0000000
## 2730 1.7159561 7.5 0.6949506 0.5325091 0.0000000
## 2731 1.7159561 7.5 0.6949506 0.5325091 0.0000000
## 2732 1.7159561 7.5 0.6949506 0.5325091 0.0000000
## 2733 1.7159561 7.5 0.6949506 0.5325091 0.0000000
## 2734 1.7159561 7.5 0.6949506 0.5325091 0.0000000
## 2735 1.7159561 7.5 0.6949506 0.5325091 0.0000000
## 2736 1.7159561 7.5 0.6949506 0.5325091 0.0000000
## 2737 1.5200182 6.5 0.4408897 0.2827829 0.0000000
## 2738 1.5200182 6.5 0.4408897 0.2827829 0.0000000
## 2739 1.5200182 6.5 0.4408897 0.2827829 0.0000000
## 2740 1.5200182 6.5 0.4408897 0.2827829 0.0000000
## 2741 1.5200182 6.5 0.4408897 0.2827829 0.0000000
## 2742 1.5200182 6.5 0.4408897 0.2827829 0.0000000
## 2743 1.5200182 6.5 0.4408897 0.2827829 0.0000000
## 2744 1.5200182 6.5 0.4408897 0.2827829 0.0000000
## 2745 1.5200182 6.5 0.4408897 0.2827829 0.0000000
## 2746 1.5200182 6.5 0.4408897 0.2827829 0.0000000
## 2747 1.5200182 6.5 0.4408897 0.2827829 0.0000000
## 2748 1.5200182 6.5 0.4408897 0.2827829 0.0000000
## 2749 1.5200182 6.5 0.4408897 0.2827829 0.0000000
## 2750 1.5200182 6.5 0.4408897 0.2827829 0.0000000
## 2751 1.5200182 6.5 0.4408897 0.2827829 0.0000000
## 2752 1.5200182 6.5 0.4408897 0.2827829 0.0000000
## 2753 1.5200182 6.5 0.4408897 0.2827829 0.0000000
## 2754 1.5200182 6.5 0.4408897 0.2827829 0.0000000
## 2755 1.5200182 6.5 0.4408897 0.2827829 0.0000000
## 2756 1.5200182 6.5 0.4408897 0.2827829 0.0000000
## 2757 1.5200182 6.5 0.4408897 0.2827829 0.0000000
## 2758 1.5200182 6.5 0.4408897 0.2827829 0.0000000
## 2759 1.5200182 6.5 0.4408897 0.2827829 0.0000000
## 2760 1.5200182 6.5 0.4408897 0.2827829 0.0000000
## 2761 1.4734648 6.0 0.5394322 0.3693305 0.0000000
## 2762 1.4734648 6.0 0.5394322 0.3693305 0.0000000
## 2763 1.4734648 6.0 0.5394322 0.3693305 0.0000000
## 2764 1.4734648 6.0 0.5394322 0.3693305 0.0000000
## 2765 1.4734648 6.0 0.5394322 0.3693305 0.0000000
## 2766 1.4734648 6.0 0.5394322 0.3693305 0.0000000
## 2767 1.4734648 6.0 0.5394322 0.3693305 0.0000000
## 2768 1.4734648 6.0 0.5394322 0.3693305 0.0000000
## 2769 1.4734648 6.0 0.5394322 0.3693305 0.0000000
## 2770 1.4734648 6.0 0.5394322 0.3693305 0.0000000
## 2771 1.4734648 6.0 0.5394322 0.3693305 0.0000000
## 2772 1.4734648 6.0 0.5394322 0.3693305 0.0000000
## 2773 1.4734648 6.0 0.5394322 0.3693305 0.0000000
## 2774 1.4734648 6.0 0.5394322 0.3693305 0.0000000
## 2775 1.4734648 6.0 0.5394322 0.3693305 0.0000000
## 2776 1.4734648 6.0 0.5394322 0.3693305 0.0000000
## 2777 1.4734648 6.0 0.5394322 0.3693305 0.0000000
## 2778 1.4734648 6.0 0.5394322 0.3693305 0.0000000
## 2779 1.4734648 6.0 0.5394322 0.3693305 0.0000000
## 2780 1.4734648 6.0 0.5394322 0.3693305 0.0000000
## 2781 1.4734648 6.0 0.5394322 0.3693305 0.0000000
## 2782 1.4734648 6.0 0.5394322 0.3693305 0.0000000
## 2783 1.4734648 6.0 0.5394322 0.3693305 0.0000000
## 2784 1.4734648 6.0 0.5394322 0.3693305 0.0000000
## 2785 1.2780964 6.0 0.5398773 0.3697479 0.2500000
## 2786 1.2780964 6.0 0.5398773 0.3697479 0.2500000
## 2787 1.2780964 6.0 0.5398773 0.3697479 0.2500000
## 2788 1.2780964 6.0 0.5398773 0.3697479 0.2500000
## 2789 1.2780964 6.0 0.5398773 0.3697479 0.2500000
## 2790 1.2780964 6.0 0.5398773 0.3697479 0.2500000
## 2791 1.2780964 6.0 0.5398773 0.3697479 0.2500000
## 2792 1.2780964 6.0 0.5398773 0.3697479 0.2500000
## 2793 1.2780964 6.0 0.5398773 0.3697479 0.2500000
## 2794 1.2780964 6.0 0.5398773 0.3697479 0.2500000
## 2795 1.2780964 6.0 0.5398773 0.3697479 0.2500000
## 2796 1.2780964 6.0 0.5398773 0.3697479 0.2500000
## 2797 1.2780964 6.0 0.5398773 0.3697479 0.2500000
## 2798 1.2780964 6.0 0.5398773 0.3697479 0.2500000
## 2799 1.2780964 6.0 0.5398773 0.3697479 0.2500000
## 2800 1.2780964 6.0 0.5398773 0.3697479 0.2500000
## 2801 1.2780964 6.0 0.5398773 0.3697479 0.2500000
## 2802 1.2780964 6.0 0.5398773 0.3697479 0.2500000
## 2803 1.2780964 6.0 0.5398773 0.3697479 0.2500000
## 2804 1.2780964 6.0 0.5398773 0.3697479 0.2500000
## 2805 1.2780964 6.0 0.5398773 0.3697479 0.2500000
## 2806 1.2780964 6.0 0.5398773 0.3697479 0.2500000
## 2807 1.2780964 6.0 0.5398773 0.3697479 0.2500000
## 2808 1.2780964 6.0 0.5398773 0.3697479 0.2500000
## 2809 1.1438811 5.0 0.8833879 0.7911323 0.5000000
## 2810 1.1438811 5.0 0.8833879 0.7911323 0.5000000
## 2811 1.1438811 5.0 0.8833879 0.7911323 0.5000000
## 2812 1.1438811 5.0 0.8833879 0.7911323 0.5000000
## 2813 1.1438811 5.0 0.8833879 0.7911323 0.5000000
## 2814 1.1438811 5.0 0.8833879 0.7911323 0.5000000
## 2815 1.1438811 5.0 0.8833879 0.7911323 0.5000000
## 2816 1.1438811 5.0 0.8833879 0.7911323 0.5000000
## 2817 1.1438811 5.0 0.8833879 0.7911323 0.5000000
## 2818 1.1438811 5.0 0.8833879 0.7911323 0.5000000
## 2819 1.1438811 5.0 0.8833879 0.7911323 0.5000000
## 2820 1.1438811 5.0 0.8833879 0.7911323 0.5000000
## 2821 1.1438811 5.0 0.8833879 0.7911323 0.5000000
## 2822 1.1438811 5.0 0.8833879 0.7911323 0.5000000
## 2823 1.1438811 5.0 0.8833879 0.7911323 0.5000000
## 2824 1.1438811 5.0 0.8833879 0.7911323 0.5000000
## 2825 1.1438811 5.0 0.8833879 0.7911323 0.5000000
## 2826 1.1438811 5.0 0.8833879 0.7911323 0.5000000
## 2827 1.1438811 5.0 0.8833879 0.7911323 0.5000000
## 2828 1.1438811 5.0 0.8833879 0.7911323 0.5000000
## 2829 1.1438811 5.0 0.8833879 0.7911323 0.5000000
## 2830 1.1438811 5.0 0.8833879 0.7911323 0.5000000
## 2831 1.1438811 5.0 0.8833879 0.7911323 0.5000000
## 2832 1.1438811 5.0 0.8833879 0.7911323 0.5000000
## 2833 1.3698145 5.5 0.9027828 0.8227931 0.0000000
## 2834 1.3698145 5.5 0.9027828 0.8227931 0.0000000
## 2835 1.3698145 5.5 0.9027828 0.8227931 0.0000000
## 2836 1.3698145 5.5 0.9027828 0.8227931 0.0000000
## 2837 1.3698145 5.5 0.9027828 0.8227931 0.0000000
## 2838 1.3698145 5.5 0.9027828 0.8227931 0.0000000
## 2839 1.3698145 5.5 0.9027828 0.8227931 0.0000000
## 2840 1.3698145 5.5 0.9027828 0.8227931 0.0000000
## 2841 1.3698145 5.5 0.9027828 0.8227931 0.0000000
## 2842 1.3698145 5.5 0.9027828 0.8227931 0.0000000
## 2843 1.3698145 5.5 0.9027828 0.8227931 0.0000000
## 2844 1.3698145 5.5 0.9027828 0.8227931 0.0000000
## 2845 1.3698145 5.5 0.9027828 0.8227931 0.0000000
## 2846 1.3698145 5.5 0.9027828 0.8227931 0.0000000
## 2847 1.3698145 5.5 0.9027828 0.8227931 0.0000000
## 2848 1.3698145 5.5 0.9027828 0.8227931 0.0000000
## 2849 1.3698145 5.5 0.9027828 0.8227931 0.0000000
## 2850 1.3698145 5.5 0.9027828 0.8227931 0.0000000
## 2851 1.3698145 5.5 0.9027828 0.8227931 0.0000000
## 2852 1.3698145 5.5 0.9027828 0.8227931 0.0000000
## 2853 1.3698145 5.5 0.9027828 0.8227931 0.0000000
## 2854 1.3698145 5.5 0.9027828 0.8227931 0.0000000
## 2855 1.3698145 5.5 0.9027828 0.8227931 0.0000000
## 2856 1.3698145 5.5 0.9027828 0.8227931 0.0000000
## 2857 0.8863028 5.0 0.9009702 0.8197869 0.5000000
## 2858 0.8863028 5.0 0.9009702 0.8197869 0.5000000
## 2859 0.8863028 5.0 0.9009702 0.8197869 0.5000000
## 2860 0.8863028 5.0 0.9009702 0.8197869 0.5000000
## 2861 0.8863028 5.0 0.9009702 0.8197869 0.5000000
## 2862 0.8863028 5.0 0.9009702 0.8197869 0.5000000
## 2863 0.8863028 5.0 0.9009702 0.8197869 0.5000000
## 2864 0.8863028 5.0 0.9009702 0.8197869 0.5000000
## 2865 0.8863028 5.0 0.9009702 0.8197869 0.5000000
## 2866 0.8863028 5.0 0.9009702 0.8197869 0.5000000
## 2867 0.8863028 5.0 0.9009702 0.8197869 0.5000000
## 2868 0.8863028 5.0 0.9009702 0.8197869 0.5000000
## 2869 0.8863028 5.0 0.9009702 0.8197869 0.5000000
## 2870 0.8863028 5.0 0.9009702 0.8197869 0.5000000
## 2871 0.8863028 5.0 0.9009702 0.8197869 0.5000000
## 2872 0.8863028 5.0 0.9009702 0.8197869 0.5000000
## 2873 0.8863028 5.0 0.9009702 0.8197869 0.5000000
## 2874 0.8863028 5.0 0.9009702 0.8197869 0.5000000
## 2875 0.8863028 5.0 0.9009702 0.8197869 0.5000000
## 2876 0.8863028 5.0 0.9009702 0.8197869 0.5000000
## 2877 0.8863028 5.0 0.9009702 0.8197869 0.5000000
## 2878 0.8863028 5.0 0.9009702 0.8197869 0.5000000
## 2879 0.8863028 5.0 0.9009702 0.8197869 0.5000000
## 2880 0.8863028 5.0 0.9009702 0.8197869 0.5000000
## 2881 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 2882 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 2883 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 2884 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 2885 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 2886 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 2887 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 2888 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 2889 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 2890 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 2891 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 2892 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 2893 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 2894 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 2895 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 2896 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 2897 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 2898 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 2899 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 2900 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 2901 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 2902 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 2903 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 2904 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 2905 1.4093629 7.5 0.3571855 0.2174229 0.1428571
## 2906 1.4093629 7.5 0.3571855 0.2174229 0.1428571
## 2907 1.4093629 7.5 0.3571855 0.2174229 0.1428571
## 2908 1.4093629 7.5 0.3571855 0.2174229 0.1428571
## 2909 1.4093629 7.5 0.3571855 0.2174229 0.1428571
## 2910 1.4093629 7.5 0.3571855 0.2174229 0.1428571
## 2911 1.4093629 7.5 0.3571855 0.2174229 0.1428571
## 2912 1.4093629 7.5 0.3571855 0.2174229 0.1428571
## 2913 1.4093629 7.5 0.3571855 0.2174229 0.1428571
## 2914 1.4093629 7.5 0.3571855 0.2174229 0.1428571
## 2915 1.4093629 7.5 0.3571855 0.2174229 0.1428571
## 2916 1.4093629 7.5 0.3571855 0.2174229 0.1428571
## 2917 1.4093629 7.5 0.3571855 0.2174229 0.1428571
## 2918 1.4093629 7.5 0.3571855 0.2174229 0.1428571
## 2919 1.4093629 7.5 0.3571855 0.2174229 0.1428571
## 2920 1.4093629 7.5 0.3571855 0.2174229 0.1428571
## 2921 1.4093629 7.5 0.3571855 0.2174229 0.1428571
## 2922 1.4093629 7.5 0.3571855 0.2174229 0.1428571
## 2923 1.4093629 7.5 0.3571855 0.2174229 0.1428571
## 2924 1.4093629 7.5 0.3571855 0.2174229 0.1428571
## 2925 1.4093629 7.5 0.3571855 0.2174229 0.1428571
## 2926 1.4093629 7.5 0.3571855 0.2174229 0.1428571
## 2927 1.4093629 7.5 0.3571855 0.2174229 0.1428571
## 2928 1.4093629 7.5 0.3571855 0.2174229 0.1428571
## 2929 1.5566020 8.0 0.4330945 0.2764012 0.2500000
## 2930 1.5566020 8.0 0.4330945 0.2764012 0.2500000
## 2931 1.5566020 8.0 0.4330945 0.2764012 0.2500000
## 2932 1.5566020 8.0 0.4330945 0.2764012 0.2500000
## 2933 1.5566020 8.0 0.4330945 0.2764012 0.2500000
## 2934 1.5566020 8.0 0.4330945 0.2764012 0.2500000
## 2935 1.5566020 8.0 0.4330945 0.2764012 0.2500000
## 2936 1.5566020 8.0 0.4330945 0.2764012 0.2500000
## 2937 1.5566020 8.0 0.4330945 0.2764012 0.2500000
## 2938 1.5566020 8.0 0.4330945 0.2764012 0.2500000
## 2939 1.5566020 8.0 0.4330945 0.2764012 0.2500000
## 2940 1.5566020 8.0 0.4330945 0.2764012 0.2500000
## 2941 1.5566020 8.0 0.4330945 0.2764012 0.2500000
## 2942 1.5566020 8.0 0.4330945 0.2764012 0.2500000
## 2943 1.5566020 8.0 0.4330945 0.2764012 0.2500000
## 2944 1.5566020 8.0 0.4330945 0.2764012 0.2500000
## 2945 1.5566020 8.0 0.4330945 0.2764012 0.2500000
## 2946 1.5566020 8.0 0.4330945 0.2764012 0.2500000
## 2947 1.5566020 8.0 0.4330945 0.2764012 0.2500000
## 2948 1.5566020 8.0 0.4330945 0.2764012 0.2500000
## 2949 1.5566020 8.0 0.4330945 0.2764012 0.2500000
## 2950 1.5566020 8.0 0.4330945 0.2764012 0.2500000
## 2951 1.5566020 8.0 0.4330945 0.2764012 0.2500000
## 2952 1.5566020 8.0 0.4330945 0.2764012 0.2500000
## 2953 1.3271205 5.0 0.7095691 0.5498699 0.0000000
## 2954 1.3271205 5.0 0.7095691 0.5498699 0.0000000
## 2955 1.3271205 5.0 0.7095691 0.5498699 0.0000000
## 2956 1.3271205 5.0 0.7095691 0.5498699 0.0000000
## 2957 1.3271205 5.0 0.7095691 0.5498699 0.0000000
## 2958 1.3271205 5.0 0.7095691 0.5498699 0.0000000
## 2959 1.3271205 5.0 0.7095691 0.5498699 0.0000000
## 2960 1.3271205 5.0 0.7095691 0.5498699 0.0000000
## 2961 1.3271205 5.0 0.7095691 0.5498699 0.0000000
## 2962 1.3271205 5.0 0.7095691 0.5498699 0.0000000
## 2963 1.3271205 5.0 0.7095691 0.5498699 0.0000000
## 2964 1.3271205 5.0 0.7095691 0.5498699 0.0000000
## 2965 1.3271205 5.0 0.7095691 0.5498699 0.0000000
## 2966 1.3271205 5.0 0.7095691 0.5498699 0.0000000
## 2967 1.3271205 5.0 0.7095691 0.5498699 0.0000000
## 2968 1.3271205 5.0 0.7095691 0.5498699 0.0000000
## 2969 1.3271205 5.0 0.7095691 0.5498699 0.0000000
## 2970 1.3271205 5.0 0.7095691 0.5498699 0.0000000
## 2971 1.3271205 5.0 0.7095691 0.5498699 0.0000000
## 2972 1.3271205 5.0 0.7095691 0.5498699 0.0000000
## 2973 1.3271205 5.0 0.7095691 0.5498699 0.0000000
## 2974 1.3271205 5.0 0.7095691 0.5498699 0.0000000
## 2975 1.3271205 5.0 0.7095691 0.5498699 0.0000000
## 2976 1.3271205 5.0 0.7095691 0.5498699 0.0000000
## 2977 1.4700682 6.0 0.5603006 0.3891789 0.0000000
## 2978 1.4700682 6.0 0.5603006 0.3891789 0.0000000
## 2979 1.4700682 6.0 0.5603006 0.3891789 0.0000000
## 2980 1.4700682 6.0 0.5603006 0.3891789 0.0000000
## 2981 1.4700682 6.0 0.5603006 0.3891789 0.0000000
## 2982 1.4700682 6.0 0.5603006 0.3891789 0.0000000
## 2983 1.4700682 6.0 0.5603006 0.3891789 0.0000000
## 2984 1.4700682 6.0 0.5603006 0.3891789 0.0000000
## 2985 1.4700682 6.0 0.5603006 0.3891789 0.0000000
## 2986 1.4700682 6.0 0.5603006 0.3891789 0.0000000
## 2987 1.4700682 6.0 0.5603006 0.3891789 0.0000000
## 2988 1.4700682 6.0 0.5603006 0.3891789 0.0000000
## 2989 1.4700682 6.0 0.5603006 0.3891789 0.0000000
## 2990 1.4700682 6.0 0.5603006 0.3891789 0.0000000
## 2991 1.4700682 6.0 0.5603006 0.3891789 0.0000000
## 2992 1.4700682 6.0 0.5603006 0.3891789 0.0000000
## 2993 1.4700682 6.0 0.5603006 0.3891789 0.0000000
## 2994 1.4700682 6.0 0.5603006 0.3891789 0.0000000
## 2995 1.4700682 6.0 0.5603006 0.3891789 0.0000000
## 2996 1.4700682 6.0 0.5603006 0.3891789 0.0000000
## 2997 1.4700682 6.0 0.5603006 0.3891789 0.0000000
## 2998 1.4700682 6.0 0.5603006 0.3891789 0.0000000
## 2999 1.4700682 6.0 0.5603006 0.3891789 0.0000000
## 3000 1.4700682 6.0 0.5603006 0.3891789 0.0000000
## 3001 1.0940609 4.5 0.8165592 0.6899874 0.0000000
## 3002 1.0940609 4.5 0.8165592 0.6899874 0.0000000
## 3003 1.0940609 4.5 0.8165592 0.6899874 0.0000000
## 3004 1.0940609 4.5 0.8165592 0.6899874 0.0000000
## 3005 1.0940609 4.5 0.8165592 0.6899874 0.0000000
## 3006 1.0940609 4.5 0.8165592 0.6899874 0.0000000
## 3007 1.0940609 4.5 0.8165592 0.6899874 0.0000000
## 3008 1.0940609 4.5 0.8165592 0.6899874 0.0000000
## 3009 1.0940609 4.5 0.8165592 0.6899874 0.0000000
## 3010 1.0940609 4.5 0.8165592 0.6899874 0.0000000
## 3011 1.0940609 4.5 0.8165592 0.6899874 0.0000000
## 3012 1.0940609 4.5 0.8165592 0.6899874 0.0000000
## 3013 1.0940609 4.5 0.8165592 0.6899874 0.0000000
## 3014 1.0940609 4.5 0.8165592 0.6899874 0.0000000
## 3015 1.0940609 4.5 0.8165592 0.6899874 0.0000000
## 3016 1.0940609 4.5 0.8165592 0.6899874 0.0000000
## 3017 1.0940609 4.5 0.8165592 0.6899874 0.0000000
## 3018 1.0940609 4.5 0.8165592 0.6899874 0.0000000
## 3019 1.0940609 4.5 0.8165592 0.6899874 0.0000000
## 3020 1.0940609 4.5 0.8165592 0.6899874 0.0000000
## 3021 1.0940609 4.5 0.8165592 0.6899874 0.0000000
## 3022 1.0940609 4.5 0.8165592 0.6899874 0.0000000
## 3023 1.0940609 4.5 0.8165592 0.6899874 0.0000000
## 3024 1.0940609 4.5 0.8165592 0.6899874 0.0000000
## 3025 0.8851120 4.5 0.9186702 0.8495744 0.5000000
## 3026 0.8851120 4.5 0.9186702 0.8495744 0.5000000
## 3027 0.8851120 4.5 0.9186702 0.8495744 0.5000000
## 3028 0.8851120 4.5 0.9186702 0.8495744 0.5000000
## 3029 0.8851120 4.5 0.9186702 0.8495744 0.5000000
## 3030 0.8851120 4.5 0.9186702 0.8495744 0.5000000
## 3031 0.8851120 4.5 0.9186702 0.8495744 0.5000000
## 3032 0.8851120 4.5 0.9186702 0.8495744 0.5000000
## 3033 0.8851120 4.5 0.9186702 0.8495744 0.5000000
## 3034 0.8851120 4.5 0.9186702 0.8495744 0.5000000
## 3035 0.8851120 4.5 0.9186702 0.8495744 0.5000000
## 3036 0.8851120 4.5 0.9186702 0.8495744 0.5000000
## 3037 0.8851120 4.5 0.9186702 0.8495744 0.5000000
## 3038 0.8851120 4.5 0.9186702 0.8495744 0.5000000
## 3039 0.8851120 4.5 0.9186702 0.8495744 0.5000000
## 3040 0.8851120 4.5 0.9186702 0.8495744 0.5000000
## 3041 0.8851120 4.5 0.9186702 0.8495744 0.5000000
## 3042 0.8851120 4.5 0.9186702 0.8495744 0.5000000
## 3043 0.8851120 4.5 0.9186702 0.8495744 0.5000000
## 3044 0.8851120 4.5 0.9186702 0.8495744 0.5000000
## 3045 0.8851120 4.5 0.9186702 0.8495744 0.5000000
## 3046 0.8851120 4.5 0.9186702 0.8495744 0.5000000
## 3047 0.8851120 4.5 0.9186702 0.8495744 0.5000000
## 3048 0.8851120 4.5 0.9186702 0.8495744 0.5000000
## 3049 1.3912750 7.0 0.8836783 0.7915983 0.0000000
## 3050 1.3912750 7.0 0.8836783 0.7915983 0.0000000
## 3051 1.3912750 7.0 0.8836783 0.7915983 0.0000000
## 3052 1.3912750 7.0 0.8836783 0.7915983 0.0000000
## 3053 1.3912750 7.0 0.8836783 0.7915983 0.0000000
## 3054 1.3912750 7.0 0.8836783 0.7915983 0.0000000
## 3055 1.3912750 7.0 0.8836783 0.7915983 0.0000000
## 3056 1.3912750 7.0 0.8836783 0.7915983 0.0000000
## 3057 1.3912750 7.0 0.8836783 0.7915983 0.0000000
## 3058 1.3912750 7.0 0.8836783 0.7915983 0.0000000
## 3059 1.3912750 7.0 0.8836783 0.7915983 0.0000000
## 3060 1.3912750 7.0 0.8836783 0.7915983 0.0000000
## 3061 1.3912750 7.0 0.8836783 0.7915983 0.0000000
## 3062 1.3912750 7.0 0.8836783 0.7915983 0.0000000
## 3063 1.3912750 7.0 0.8836783 0.7915983 0.0000000
## 3064 1.3912750 7.0 0.8836783 0.7915983 0.0000000
## 3065 1.3912750 7.0 0.8836783 0.7915983 0.0000000
## 3066 1.3912750 7.0 0.8836783 0.7915983 0.0000000
## 3067 1.3912750 7.0 0.8836783 0.7915983 0.0000000
## 3068 1.3912750 7.0 0.8836783 0.7915983 0.0000000
## 3069 1.3912750 7.0 0.8836783 0.7915983 0.0000000
## 3070 1.3912750 7.0 0.8836783 0.7915983 0.0000000
## 3071 1.3912750 7.0 0.8836783 0.7915983 0.0000000
## 3072 1.3912750 7.0 0.8836783 0.7915983 0.0000000
## 3073 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 3074 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 3075 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 3076 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 3077 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 3078 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 3079 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 3080 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 3081 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 3082 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 3083 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 3084 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 3085 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 3086 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 3087 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 3088 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 3089 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 3090 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 3091 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 3092 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 3093 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 3094 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 3095 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 3096 2.0784670 11.0 0.0000000 0.0000000 0.0000000
## 3097 1.6434662 8.0 0.6412607 0.4719528 0.1428571
## 3098 1.6434662 8.0 0.6412607 0.4719528 0.1428571
## 3099 1.6434662 8.0 0.6412607 0.4719528 0.1428571
## 3100 1.6434662 8.0 0.6412607 0.4719528 0.1428571
## 3101 1.6434662 8.0 0.6412607 0.4719528 0.1428571
## 3102 1.6434662 8.0 0.6412607 0.4719528 0.1428571
## 3103 1.6434662 8.0 0.6412607 0.4719528 0.1428571
## 3104 1.6434662 8.0 0.6412607 0.4719528 0.1428571
## 3105 1.6434662 8.0 0.6412607 0.4719528 0.1428571
## 3106 1.6434662 8.0 0.6412607 0.4719528 0.1428571
## 3107 1.6434662 8.0 0.6412607 0.4719528 0.1428571
## 3108 1.6434662 8.0 0.6412607 0.4719528 0.1428571
## 3109 1.6434662 8.0 0.6412607 0.4719528 0.1428571
## 3110 1.6434662 8.0 0.6412607 0.4719528 0.1428571
## 3111 1.6434662 8.0 0.6412607 0.4719528 0.1428571
## 3112 1.6434662 8.0 0.6412607 0.4719528 0.1428571
## 3113 1.6434662 8.0 0.6412607 0.4719528 0.1428571
## 3114 1.6434662 8.0 0.6412607 0.4719528 0.1428571
## 3115 1.6434662 8.0 0.6412607 0.4719528 0.1428571
## 3116 1.6434662 8.0 0.6412607 0.4719528 0.1428571
## 3117 1.6434662 8.0 0.6412607 0.4719528 0.1428571
## 3118 1.6434662 8.0 0.6412607 0.4719528 0.1428571
## 3119 1.6434662 8.0 0.6412607 0.4719528 0.1428571
## 3120 1.6434662 8.0 0.6412607 0.4719528 0.1428571
## 3121 1.4835587 6.5 0.3623978 0.2212978 0.0000000
## 3122 1.4835587 6.5 0.3623978 0.2212978 0.0000000
## 3123 1.4835587 6.5 0.3623978 0.2212978 0.0000000
## 3124 1.4835587 6.5 0.3623978 0.2212978 0.0000000
## 3125 1.4835587 6.5 0.3623978 0.2212978 0.0000000
## 3126 1.4835587 6.5 0.3623978 0.2212978 0.0000000
## 3127 1.4835587 6.5 0.3623978 0.2212978 0.0000000
## 3128 1.4835587 6.5 0.3623978 0.2212978 0.0000000
## 3129 1.4835587 6.5 0.3623978 0.2212978 0.0000000
## 3130 1.4835587 6.5 0.3623978 0.2212978 0.0000000
## 3131 1.4835587 6.5 0.3623978 0.2212978 0.0000000
## 3132 1.4835587 6.5 0.3623978 0.2212978 0.0000000
## 3133 1.4835587 6.5 0.3623978 0.2212978 0.0000000
## 3134 1.4835587 6.5 0.3623978 0.2212978 0.0000000
## 3135 1.4835587 6.5 0.3623978 0.2212978 0.0000000
## 3136 1.4835587 6.5 0.3623978 0.2212978 0.0000000
## 3137 1.4835587 6.5 0.3623978 0.2212978 0.0000000
## 3138 1.4835587 6.5 0.3623978 0.2212978 0.0000000
## 3139 1.4835587 6.5 0.3623978 0.2212978 0.0000000
## 3140 1.4835587 6.5 0.3623978 0.2212978 0.0000000
## 3141 1.4835587 6.5 0.3623978 0.2212978 0.0000000
## 3142 1.4835587 6.5 0.3623978 0.2212978 0.0000000
## 3143 1.4835587 6.5 0.3623978 0.2212978 0.0000000
## 3144 1.4835587 6.5 0.3623978 0.2212978 0.0000000
## 3145 1.2463855 5.0 0.7793072 0.6384138 0.0000000
## 3146 1.2463855 5.0 0.7793072 0.6384138 0.0000000
## 3147 1.2463855 5.0 0.7793072 0.6384138 0.0000000
## 3148 1.2463855 5.0 0.7793072 0.6384138 0.0000000
## 3149 1.2463855 5.0 0.7793072 0.6384138 0.0000000
## 3150 1.2463855 5.0 0.7793072 0.6384138 0.0000000
## 3151 1.2463855 5.0 0.7793072 0.6384138 0.0000000
## 3152 1.2463855 5.0 0.7793072 0.6384138 0.0000000
## 3153 1.2463855 5.0 0.7793072 0.6384138 0.0000000
## 3154 1.2463855 5.0 0.7793072 0.6384138 0.0000000
## 3155 1.2463855 5.0 0.7793072 0.6384138 0.0000000
## 3156 1.2463855 5.0 0.7793072 0.6384138 0.0000000
## 3157 1.2463855 5.0 0.7793072 0.6384138 0.0000000
## 3158 1.2463855 5.0 0.7793072 0.6384138 0.0000000
## 3159 1.2463855 5.0 0.7793072 0.6384138 0.0000000
## 3160 1.2463855 5.0 0.7793072 0.6384138 0.0000000
## 3161 1.2463855 5.0 0.7793072 0.6384138 0.0000000
## 3162 1.2463855 5.0 0.7793072 0.6384138 0.0000000
## 3163 1.2463855 5.0 0.7793072 0.6384138 0.0000000
## 3164 1.2463855 5.0 0.7793072 0.6384138 0.0000000
## 3165 1.2463855 5.0 0.7793072 0.6384138 0.0000000
## 3166 1.2463855 5.0 0.7793072 0.6384138 0.0000000
## 3167 1.2463855 5.0 0.7793072 0.6384138 0.0000000
## 3168 1.2463855 5.0 0.7793072 0.6384138 0.0000000
## 3169 1.2432838 4.5 0.6892984 0.5259003 0.2500000
## 3170 1.2432838 4.5 0.6892984 0.5259003 0.2500000
## 3171 1.2432838 4.5 0.6892984 0.5259003 0.2500000
## 3172 1.2432838 4.5 0.6892984 0.5259003 0.2500000
## 3173 1.2432838 4.5 0.6892984 0.5259003 0.2500000
## 3174 1.2432838 4.5 0.6892984 0.5259003 0.2500000
## 3175 1.2432838 4.5 0.6892984 0.5259003 0.2500000
## 3176 1.2432838 4.5 0.6892984 0.5259003 0.2500000
## 3177 1.2432838 4.5 0.6892984 0.5259003 0.2500000
## 3178 1.2432838 4.5 0.6892984 0.5259003 0.2500000
## 3179 1.2432838 4.5 0.6892984 0.5259003 0.2500000
## 3180 1.2432838 4.5 0.6892984 0.5259003 0.2500000
## 3181 1.2432838 4.5 0.6892984 0.5259003 0.2500000
## 3182 1.2432838 4.5 0.6892984 0.5259003 0.2500000
## 3183 1.2432838 4.5 0.6892984 0.5259003 0.2500000
## 3184 1.2432838 4.5 0.6892984 0.5259003 0.2500000
## 3185 1.2432838 4.5 0.6892984 0.5259003 0.2500000
## 3186 1.2432838 4.5 0.6892984 0.5259003 0.2500000
## 3187 1.2432838 4.5 0.6892984 0.5259003 0.2500000
## 3188 1.2432838 4.5 0.6892984 0.5259003 0.2500000
## 3189 1.2432838 4.5 0.6892984 0.5259003 0.2500000
## 3190 1.2432838 4.5 0.6892984 0.5259003 0.2500000
## 3191 1.2432838 4.5 0.6892984 0.5259003 0.2500000
## 3192 1.2432838 4.5 0.6892984 0.5259003 0.2500000
## 3193 1.3041770 5.5 0.8280669 0.7065821 0.0000000
## 3194 1.3041770 5.5 0.8280669 0.7065821 0.0000000
## 3195 1.3041770 5.5 0.8280669 0.7065821 0.0000000
## 3196 1.3041770 5.5 0.8280669 0.7065821 0.0000000
## 3197 1.3041770 5.5 0.8280669 0.7065821 0.0000000
## 3198 1.3041770 5.5 0.8280669 0.7065821 0.0000000
## 3199 1.3041770 5.5 0.8280669 0.7065821 0.0000000
## 3200 1.3041770 5.5 0.8280669 0.7065821 0.0000000
## 3201 1.3041770 5.5 0.8280669 0.7065821 0.0000000
## 3202 1.3041770 5.5 0.8280669 0.7065821 0.0000000
## 3203 1.3041770 5.5 0.8280669 0.7065821 0.0000000
## 3204 1.3041770 5.5 0.8280669 0.7065821 0.0000000
## 3205 1.3041770 5.5 0.8280669 0.7065821 0.0000000
## 3206 1.3041770 5.5 0.8280669 0.7065821 0.0000000
## 3207 1.3041770 5.5 0.8280669 0.7065821 0.0000000
## 3208 1.3041770 5.5 0.8280669 0.7065821 0.0000000
## 3209 1.3041770 5.5 0.8280669 0.7065821 0.0000000
## 3210 1.3041770 5.5 0.8280669 0.7065821 0.0000000
## 3211 1.3041770 5.5 0.8280669 0.7065821 0.0000000
## 3212 1.3041770 5.5 0.8280669 0.7065821 0.0000000
## 3213 1.3041770 5.5 0.8280669 0.7065821 0.0000000
## 3214 1.3041770 5.5 0.8280669 0.7065821 0.0000000
## 3215 1.3041770 5.5 0.8280669 0.7065821 0.0000000
## 3216 1.3041770 5.5 0.8280669 0.7065821 0.0000000
## 3217 0.9845120 4.5 0.8706428 0.7709188 0.0000000
## 3218 0.9845120 4.5 0.8706428 0.7709188 0.0000000
## 3219 0.9845120 4.5 0.8706428 0.7709188 0.0000000
## 3220 0.9845120 4.5 0.8706428 0.7709188 0.0000000
## 3221 0.9845120 4.5 0.8706428 0.7709188 0.0000000
## 3222 0.9845120 4.5 0.8706428 0.7709188 0.0000000
## 3223 0.9845120 4.5 0.8706428 0.7709188 0.0000000
## 3224 0.9845120 4.5 0.8706428 0.7709188 0.0000000
## 3225 0.9845120 4.5 0.8706428 0.7709188 0.0000000
## beta_nestedness beta_total metaecosystem_richness
## 1 0.00000000 0.00000000 11
## 2 0.00000000 0.00000000 11
## 3 0.00000000 0.00000000 11
## 4 0.00000000 0.00000000 11
## 5 0.00000000 0.00000000 11
## 6 0.00000000 0.00000000 11
## 7 0.00000000 0.00000000 11
## 8 0.00000000 0.00000000 11
## 9 0.00000000 0.00000000 11
## 10 0.00000000 0.00000000 11
## 11 0.00000000 0.00000000 11
## 12 0.00000000 0.00000000 11
## 13 0.00000000 0.00000000 11
## 14 0.00000000 0.00000000 11
## 15 0.00000000 0.00000000 11
## 16 0.00000000 0.00000000 11
## 17 0.00000000 0.00000000 11
## 18 0.00000000 0.00000000 11
## 19 0.00000000 0.00000000 11
## 20 0.00000000 0.00000000 11
## 21 0.00000000 0.00000000 11
## 22 0.00000000 0.00000000 11
## 23 0.00000000 0.00000000 11
## 24 0.00000000 0.00000000 11
## 25 0.05714286 0.20000000 9
## 26 0.05714286 0.20000000 9
## 27 0.05714286 0.20000000 9
## 28 0.05714286 0.20000000 9
## 29 0.05714286 0.20000000 9
## 30 0.05714286 0.20000000 9
## 31 0.05714286 0.20000000 9
## 32 0.05714286 0.20000000 9
## 33 0.05714286 0.20000000 9
## 34 0.05714286 0.20000000 9
## 35 0.05714286 0.20000000 9
## 36 0.05714286 0.20000000 9
## 37 0.05714286 0.20000000 9
## 38 0.05714286 0.20000000 9
## 39 0.05714286 0.20000000 9
## 40 0.05714286 0.20000000 9
## 41 0.05714286 0.20000000 9
## 42 0.05714286 0.20000000 9
## 43 0.05714286 0.20000000 9
## 44 0.05714286 0.20000000 9
## 45 0.05714286 0.20000000 9
## 46 0.05714286 0.20000000 9
## 47 0.05714286 0.20000000 9
## 48 0.05714286 0.20000000 9
## 49 0.00000000 0.12500000 9
## 50 0.00000000 0.12500000 9
## 51 0.00000000 0.12500000 9
## 52 0.00000000 0.12500000 9
## 53 0.00000000 0.12500000 9
## 54 0.00000000 0.12500000 9
## 55 0.00000000 0.12500000 9
## 56 0.00000000 0.12500000 9
## 57 0.00000000 0.12500000 9
## 58 0.00000000 0.12500000 9
## 59 0.00000000 0.12500000 9
## 60 0.00000000 0.12500000 9
## 61 0.00000000 0.12500000 9
## 62 0.00000000 0.12500000 9
## 63 0.00000000 0.12500000 9
## 64 0.00000000 0.12500000 9
## 65 0.00000000 0.12500000 9
## 66 0.00000000 0.12500000 9
## 67 0.00000000 0.12500000 9
## 68 0.00000000 0.12500000 9
## 69 0.00000000 0.12500000 9
## 70 0.00000000 0.12500000 9
## 71 0.00000000 0.12500000 9
## 72 0.00000000 0.12500000 9
## 73 0.33333333 0.33333333 8
## 74 0.33333333 0.33333333 8
## 75 0.33333333 0.33333333 8
## 76 0.33333333 0.33333333 8
## 77 0.33333333 0.33333333 8
## 78 0.33333333 0.33333333 8
## 79 0.33333333 0.33333333 8
## 80 0.33333333 0.33333333 8
## 81 0.33333333 0.33333333 8
## 82 0.33333333 0.33333333 8
## 83 0.33333333 0.33333333 8
## 84 0.33333333 0.33333333 8
## 85 0.33333333 0.33333333 8
## 86 0.33333333 0.33333333 8
## 87 0.33333333 0.33333333 8
## 88 0.33333333 0.33333333 8
## 89 0.33333333 0.33333333 8
## 90 0.33333333 0.33333333 8
## 91 0.33333333 0.33333333 8
## 92 0.33333333 0.33333333 8
## 93 0.33333333 0.33333333 8
## 94 0.33333333 0.33333333 8
## 95 0.33333333 0.33333333 8
## 96 0.33333333 0.33333333 8
## 97 0.05714286 0.20000000 9
## 98 0.05714286 0.20000000 9
## 99 0.05714286 0.20000000 9
## 100 0.05714286 0.20000000 9
## 101 0.05714286 0.20000000 9
## 102 0.05714286 0.20000000 9
## 103 0.05714286 0.20000000 9
## 104 0.05714286 0.20000000 9
## 105 0.05714286 0.20000000 9
## 106 0.05714286 0.20000000 9
## 107 0.05714286 0.20000000 9
## 108 0.05714286 0.20000000 9
## 109 0.05714286 0.20000000 9
## 110 0.05714286 0.20000000 9
## 111 0.05714286 0.20000000 9
## 112 0.05714286 0.20000000 9
## 113 0.05714286 0.20000000 9
## 114 0.05714286 0.20000000 9
## 115 0.05714286 0.20000000 9
## 116 0.05714286 0.20000000 9
## 117 0.05714286 0.20000000 9
## 118 0.05714286 0.20000000 9
## 119 0.05714286 0.20000000 9
## 120 0.05714286 0.20000000 9
## 121 0.50000000 0.50000000 6
## 122 0.50000000 0.50000000 6
## 123 0.50000000 0.50000000 6
## 124 0.50000000 0.50000000 6
## 125 0.50000000 0.50000000 6
## 126 0.50000000 0.50000000 6
## 127 0.50000000 0.50000000 6
## 128 0.50000000 0.50000000 6
## 129 0.50000000 0.50000000 6
## 130 0.50000000 0.50000000 6
## 131 0.50000000 0.50000000 6
## 132 0.50000000 0.50000000 6
## 133 0.50000000 0.50000000 6
## 134 0.50000000 0.50000000 6
## 135 0.50000000 0.50000000 6
## 136 0.50000000 0.50000000 6
## 137 0.50000000 0.50000000 6
## 138 0.50000000 0.50000000 6
## 139 0.50000000 0.50000000 6
## 140 0.50000000 0.50000000 6
## 141 0.50000000 0.50000000 6
## 142 0.50000000 0.50000000 6
## 143 0.50000000 0.50000000 6
## 144 0.50000000 0.50000000 6
## 145 0.10000000 0.50000000 9
## 146 0.10000000 0.50000000 9
## 147 0.10000000 0.50000000 9
## 148 0.10000000 0.50000000 9
## 149 0.10000000 0.50000000 9
## 150 0.10000000 0.50000000 9
## 151 0.10000000 0.50000000 9
## 152 0.10000000 0.50000000 9
## 153 0.10000000 0.50000000 9
## 154 0.10000000 0.50000000 9
## 155 0.10000000 0.50000000 9
## 156 0.10000000 0.50000000 9
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## 1917 0.77777778 0.77777778 8
## 1918 0.77777778 0.77777778 8
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## 1986 0.14285714 0.14285714 8
## 1987 0.14285714 0.14285714 8
## 1988 0.14285714 0.14285714 8
## 1989 0.14285714 0.14285714 8
## 1990 0.14285714 0.14285714 8
## 1991 0.14285714 0.14285714 8
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## 2887 0.00000000 0.00000000 11
## 2888 0.00000000 0.00000000 11
## 2889 0.00000000 0.00000000 11
## 2890 0.00000000 0.00000000 11
## 2891 0.00000000 0.00000000 11
## 2892 0.00000000 0.00000000 11
## 2893 0.00000000 0.00000000 11
## 2894 0.00000000 0.00000000 11
## 2895 0.00000000 0.00000000 11
## 2896 0.00000000 0.00000000 11
## 2897 0.00000000 0.00000000 11
## 2898 0.00000000 0.00000000 11
## 2899 0.00000000 0.00000000 11
## 2900 0.00000000 0.00000000 11
## 2901 0.00000000 0.00000000 11
## 2902 0.00000000 0.00000000 11
## 2903 0.00000000 0.00000000 11
## 2904 0.00000000 0.00000000 11
## 2905 0.05714286 0.20000000 9
## 2906 0.05714286 0.20000000 9
## 2907 0.05714286 0.20000000 9
## 2908 0.05714286 0.20000000 9
## 2909 0.05714286 0.20000000 9
## 2910 0.05714286 0.20000000 9
## 2911 0.05714286 0.20000000 9
## 2912 0.05714286 0.20000000 9
## 2913 0.05714286 0.20000000 9
## 2914 0.05714286 0.20000000 9
## 2915 0.05714286 0.20000000 9
## 2916 0.05714286 0.20000000 9
## 2917 0.05714286 0.20000000 9
## 2918 0.05714286 0.20000000 9
## 2919 0.05714286 0.20000000 9
## 2920 0.05714286 0.20000000 9
## 2921 0.05714286 0.20000000 9
## 2922 0.05714286 0.20000000 9
## 2923 0.05714286 0.20000000 9
## 2924 0.05714286 0.20000000 9
## 2925 0.05714286 0.20000000 9
## 2926 0.05714286 0.20000000 9
## 2927 0.05714286 0.20000000 9
## 2928 0.05714286 0.20000000 9
## 2929 0.00000000 0.25000000 10
## 2930 0.00000000 0.25000000 10
## 2931 0.00000000 0.25000000 10
## 2932 0.00000000 0.25000000 10
## 2933 0.00000000 0.25000000 10
## 2934 0.00000000 0.25000000 10
## 2935 0.00000000 0.25000000 10
## 2936 0.00000000 0.25000000 10
## 2937 0.00000000 0.25000000 10
## 2938 0.00000000 0.25000000 10
## 2939 0.00000000 0.25000000 10
## 2940 0.00000000 0.25000000 10
## 2941 0.00000000 0.25000000 10
## 2942 0.00000000 0.25000000 10
## 2943 0.00000000 0.25000000 10
## 2944 0.00000000 0.25000000 10
## 2945 0.00000000 0.25000000 10
## 2946 0.00000000 0.25000000 10
## 2947 0.00000000 0.25000000 10
## 2948 0.00000000 0.25000000 10
## 2949 0.00000000 0.25000000 10
## 2950 0.00000000 0.25000000 10
## 2951 0.00000000 0.25000000 10
## 2952 0.00000000 0.25000000 10
## 2953 0.60000000 0.60000000 8
## 2954 0.60000000 0.60000000 8
## 2955 0.60000000 0.60000000 8
## 2956 0.60000000 0.60000000 8
## 2957 0.60000000 0.60000000 8
## 2958 0.60000000 0.60000000 8
## 2959 0.60000000 0.60000000 8
## 2960 0.60000000 0.60000000 8
## 2961 0.60000000 0.60000000 8
## 2962 0.60000000 0.60000000 8
## 2963 0.60000000 0.60000000 8
## 2964 0.60000000 0.60000000 8
## 2965 0.60000000 0.60000000 8
## 2966 0.60000000 0.60000000 8
## 2967 0.60000000 0.60000000 8
## 2968 0.60000000 0.60000000 8
## 2969 0.60000000 0.60000000 8
## 2970 0.60000000 0.60000000 8
## 2971 0.60000000 0.60000000 8
## 2972 0.60000000 0.60000000 8
## 2973 0.60000000 0.60000000 8
## 2974 0.60000000 0.60000000 8
## 2975 0.60000000 0.60000000 8
## 2976 0.60000000 0.60000000 8
## 2977 0.33333333 0.33333333 8
## 2978 0.33333333 0.33333333 8
## 2979 0.33333333 0.33333333 8
## 2980 0.33333333 0.33333333 8
## 2981 0.33333333 0.33333333 8
## 2982 0.33333333 0.33333333 8
## 2983 0.33333333 0.33333333 8
## 2984 0.33333333 0.33333333 8
## 2985 0.33333333 0.33333333 8
## 2986 0.33333333 0.33333333 8
## 2987 0.33333333 0.33333333 8
## 2988 0.33333333 0.33333333 8
## 2989 0.33333333 0.33333333 8
## 2990 0.33333333 0.33333333 8
## 2991 0.33333333 0.33333333 8
## 2992 0.33333333 0.33333333 8
## 2993 0.33333333 0.33333333 8
## 2994 0.33333333 0.33333333 8
## 2995 0.33333333 0.33333333 8
## 2996 0.33333333 0.33333333 8
## 2997 0.33333333 0.33333333 8
## 2998 0.33333333 0.33333333 8
## 2999 0.33333333 0.33333333 8
## 3000 0.33333333 0.33333333 8
## 3001 0.33333333 0.33333333 6
## 3002 0.33333333 0.33333333 6
## 3003 0.33333333 0.33333333 6
## 3004 0.33333333 0.33333333 6
## 3005 0.33333333 0.33333333 6
## 3006 0.33333333 0.33333333 6
## 3007 0.33333333 0.33333333 6
## 3008 0.33333333 0.33333333 6
## 3009 0.33333333 0.33333333 6
## 3010 0.33333333 0.33333333 6
## 3011 0.33333333 0.33333333 6
## 3012 0.33333333 0.33333333 6
## 3013 0.33333333 0.33333333 6
## 3014 0.33333333 0.33333333 6
## 3015 0.33333333 0.33333333 6
## 3016 0.33333333 0.33333333 6
## 3017 0.33333333 0.33333333 6
## 3018 0.33333333 0.33333333 6
## 3019 0.33333333 0.33333333 6
## 3020 0.33333333 0.33333333 6
## 3021 0.33333333 0.33333333 6
## 3022 0.33333333 0.33333333 6
## 3023 0.33333333 0.33333333 6
## 3024 0.33333333 0.33333333 6
## 3025 0.27777778 0.77777778 8
## 3026 0.27777778 0.77777778 8
## 3027 0.27777778 0.77777778 8
## 3028 0.27777778 0.77777778 8
## 3029 0.27777778 0.77777778 8
## 3030 0.27777778 0.77777778 8
## 3031 0.27777778 0.77777778 8
## 3032 0.27777778 0.77777778 8
## 3033 0.27777778 0.77777778 8
## 3034 0.27777778 0.77777778 8
## 3035 0.27777778 0.77777778 8
## 3036 0.27777778 0.77777778 8
## 3037 0.27777778 0.77777778 8
## 3038 0.27777778 0.77777778 8
## 3039 0.27777778 0.77777778 8
## 3040 0.27777778 0.77777778 8
## 3041 0.27777778 0.77777778 8
## 3042 0.27777778 0.77777778 8
## 3043 0.27777778 0.77777778 8
## 3044 0.27777778 0.77777778 8
## 3045 0.27777778 0.77777778 8
## 3046 0.27777778 0.77777778 8
## 3047 0.27777778 0.77777778 8
## 3048 0.27777778 0.77777778 8
## 3049 0.28571429 0.28571429 9
## 3050 0.28571429 0.28571429 9
## 3051 0.28571429 0.28571429 9
## 3052 0.28571429 0.28571429 9
## 3053 0.28571429 0.28571429 9
## 3054 0.28571429 0.28571429 9
## 3055 0.28571429 0.28571429 9
## 3056 0.28571429 0.28571429 9
## 3057 0.28571429 0.28571429 9
## 3058 0.28571429 0.28571429 9
## 3059 0.28571429 0.28571429 9
## 3060 0.28571429 0.28571429 9
## 3061 0.28571429 0.28571429 9
## 3062 0.28571429 0.28571429 9
## 3063 0.28571429 0.28571429 9
## 3064 0.28571429 0.28571429 9
## 3065 0.28571429 0.28571429 9
## 3066 0.28571429 0.28571429 9
## 3067 0.28571429 0.28571429 9
## 3068 0.28571429 0.28571429 9
## 3069 0.28571429 0.28571429 9
## 3070 0.28571429 0.28571429 9
## 3071 0.28571429 0.28571429 9
## 3072 0.28571429 0.28571429 9
## 3073 0.00000000 0.00000000 11
## 3074 0.00000000 0.00000000 11
## 3075 0.00000000 0.00000000 11
## 3076 0.00000000 0.00000000 11
## 3077 0.00000000 0.00000000 11
## 3078 0.00000000 0.00000000 11
## 3079 0.00000000 0.00000000 11
## 3080 0.00000000 0.00000000 11
## 3081 0.00000000 0.00000000 11
## 3082 0.00000000 0.00000000 11
## 3083 0.00000000 0.00000000 11
## 3084 0.00000000 0.00000000 11
## 3085 0.00000000 0.00000000 11
## 3086 0.00000000 0.00000000 11
## 3087 0.00000000 0.00000000 11
## 3088 0.00000000 0.00000000 11
## 3089 0.00000000 0.00000000 11
## 3090 0.00000000 0.00000000 11
## 3091 0.00000000 0.00000000 11
## 3092 0.00000000 0.00000000 11
## 3093 0.00000000 0.00000000 11
## 3094 0.00000000 0.00000000 11
## 3095 0.00000000 0.00000000 11
## 3096 0.00000000 0.00000000 11
## 3097 0.10714286 0.25000000 10
## 3098 0.10714286 0.25000000 10
## 3099 0.10714286 0.25000000 10
## 3100 0.10714286 0.25000000 10
## 3101 0.10714286 0.25000000 10
## 3102 0.10714286 0.25000000 10
## 3103 0.10714286 0.25000000 10
## 3104 0.10714286 0.25000000 10
## 3105 0.10714286 0.25000000 10
## 3106 0.10714286 0.25000000 10
## 3107 0.10714286 0.25000000 10
## 3108 0.10714286 0.25000000 10
## 3109 0.10714286 0.25000000 10
## 3110 0.10714286 0.25000000 10
## 3111 0.10714286 0.25000000 10
## 3112 0.10714286 0.25000000 10
## 3113 0.10714286 0.25000000 10
## 3114 0.10714286 0.25000000 10
## 3115 0.10714286 0.25000000 10
## 3116 0.10714286 0.25000000 10
## 3117 0.10714286 0.25000000 10
## 3118 0.10714286 0.25000000 10
## 3119 0.10714286 0.25000000 10
## 3120 0.10714286 0.25000000 10
## 3121 0.23076923 0.23076923 8
## 3122 0.23076923 0.23076923 8
## 3123 0.23076923 0.23076923 8
## 3124 0.23076923 0.23076923 8
## 3125 0.23076923 0.23076923 8
## 3126 0.23076923 0.23076923 8
## 3127 0.23076923 0.23076923 8
## 3128 0.23076923 0.23076923 8
## 3129 0.23076923 0.23076923 8
## 3130 0.23076923 0.23076923 8
## 3131 0.23076923 0.23076923 8
## 3132 0.23076923 0.23076923 8
## 3133 0.23076923 0.23076923 8
## 3134 0.23076923 0.23076923 8
## 3135 0.23076923 0.23076923 8
## 3136 0.23076923 0.23076923 8
## 3137 0.23076923 0.23076923 8
## 3138 0.23076923 0.23076923 8
## 3139 0.23076923 0.23076923 8
## 3140 0.23076923 0.23076923 8
## 3141 0.23076923 0.23076923 8
## 3142 0.23076923 0.23076923 8
## 3143 0.23076923 0.23076923 8
## 3144 0.23076923 0.23076923 8
## 3145 0.60000000 0.60000000 8
## 3146 0.60000000 0.60000000 8
## 3147 0.60000000 0.60000000 8
## 3148 0.60000000 0.60000000 8
## 3149 0.60000000 0.60000000 8
## 3150 0.60000000 0.60000000 8
## 3151 0.60000000 0.60000000 8
## 3152 0.60000000 0.60000000 8
## 3153 0.60000000 0.60000000 8
## 3154 0.60000000 0.60000000 8
## 3155 0.60000000 0.60000000 8
## 3156 0.60000000 0.60000000 8
## 3157 0.60000000 0.60000000 8
## 3158 0.60000000 0.60000000 8
## 3159 0.60000000 0.60000000 8
## 3160 0.60000000 0.60000000 8
## 3161 0.60000000 0.60000000 8
## 3162 0.60000000 0.60000000 8
## 3163 0.60000000 0.60000000 8
## 3164 0.60000000 0.60000000 8
## 3165 0.60000000 0.60000000 8
## 3166 0.60000000 0.60000000 8
## 3167 0.60000000 0.60000000 8
## 3168 0.60000000 0.60000000 8
## 3169 0.08333333 0.33333333 6
## 3170 0.08333333 0.33333333 6
## 3171 0.08333333 0.33333333 6
## 3172 0.08333333 0.33333333 6
## 3173 0.08333333 0.33333333 6
## 3174 0.08333333 0.33333333 6
## 3175 0.08333333 0.33333333 6
## 3176 0.08333333 0.33333333 6
## 3177 0.08333333 0.33333333 6
## 3178 0.08333333 0.33333333 6
## 3179 0.08333333 0.33333333 6
## 3180 0.08333333 0.33333333 6
## 3181 0.08333333 0.33333333 6
## 3182 0.08333333 0.33333333 6
## 3183 0.08333333 0.33333333 6
## 3184 0.08333333 0.33333333 6
## 3185 0.08333333 0.33333333 6
## 3186 0.08333333 0.33333333 6
## 3187 0.08333333 0.33333333 6
## 3188 0.08333333 0.33333333 6
## 3189 0.08333333 0.33333333 6
## 3190 0.08333333 0.33333333 6
## 3191 0.08333333 0.33333333 6
## 3192 0.08333333 0.33333333 6
## 3193 0.45454545 0.45454545 8
## 3194 0.45454545 0.45454545 8
## 3195 0.45454545 0.45454545 8
## 3196 0.45454545 0.45454545 8
## 3197 0.45454545 0.45454545 8
## 3198 0.45454545 0.45454545 8
## 3199 0.45454545 0.45454545 8
## 3200 0.45454545 0.45454545 8
## 3201 0.45454545 0.45454545 8
## 3202 0.45454545 0.45454545 8
## 3203 0.45454545 0.45454545 8
## 3204 0.45454545 0.45454545 8
## 3205 0.45454545 0.45454545 8
## 3206 0.45454545 0.45454545 8
## 3207 0.45454545 0.45454545 8
## 3208 0.45454545 0.45454545 8
## 3209 0.45454545 0.45454545 8
## 3210 0.45454545 0.45454545 8
## 3211 0.45454545 0.45454545 8
## 3212 0.45454545 0.45454545 8
## 3213 0.45454545 0.45454545 8
## 3214 0.45454545 0.45454545 8
## 3215 0.45454545 0.45454545 8
## 3216 0.45454545 0.45454545 8
## 3217 0.55555556 0.55555556 7
## 3218 0.55555556 0.55555556 7
## 3219 0.55555556 0.55555556 7
## 3220 0.55555556 0.55555556 7
## 3221 0.55555556 0.55555556 7
## 3222 0.55555556 0.55555556 7
## 3223 0.55555556 0.55555556 7
## 3224 0.55555556 0.55555556 7
## 3225 0.55555556 0.55555556 7
## total_metaecosystem_bioarea_mm2 total_metaecosystem_Ble_indiv
## 1 96.92197 217.1512
## 2 96.92197 217.1512
## 3 96.92197 217.1512
## 4 96.92197 217.1512
## 5 96.92197 217.1512
## 6 96.92197 217.1512
## 7 96.92197 217.1512
## 8 96.92197 217.1512
## 9 96.92197 217.1512
## 10 96.92197 217.1512
## 11 96.92197 217.1512
## 12 96.92197 217.1512
## 13 96.92197 217.1512
## 14 96.92197 217.1512
## 15 96.92197 217.1512
## 16 96.92197 217.1512
## 17 96.92197 217.1512
## 18 96.92197 217.1512
## 19 96.92197 217.1512
## 20 96.92197 217.1512
## 21 96.92197 217.1512
## 22 96.92197 217.1512
## 23 96.92197 217.1512
## 24 96.92197 217.1512
## 25 152.90836 1037.7907
## 26 152.90836 1037.7907
## 27 152.90836 1037.7907
## 28 152.90836 1037.7907
## 29 152.90836 1037.7907
## 30 152.90836 1037.7907
## 31 152.90836 1037.7907
## 32 152.90836 1037.7907
## 33 152.90836 1037.7907
## 34 152.90836 1037.7907
## 35 152.90836 1037.7907
## 36 152.90836 1037.7907
## 37 152.90836 1037.7907
## 38 152.90836 1037.7907
## 39 152.90836 1037.7907
## 40 152.90836 1037.7907
## 41 152.90836 1037.7907
## 42 152.90836 1037.7907
## 43 152.90836 1037.7907
## 44 152.90836 1037.7907
## 45 152.90836 1037.7907
## 46 152.90836 1037.7907
## 47 152.90836 1037.7907
## 48 152.90836 1037.7907
## 49 163.09305 2398.2558
## 50 163.09305 2398.2558
## 51 163.09305 2398.2558
## 52 163.09305 2398.2558
## 53 163.09305 2398.2558
## 54 163.09305 2398.2558
## 55 163.09305 2398.2558
## 56 163.09305 2398.2558
## 57 163.09305 2398.2558
## 58 163.09305 2398.2558
## 59 163.09305 2398.2558
## 60 163.09305 2398.2558
## 61 163.09305 2398.2558
## 62 163.09305 2398.2558
## 63 163.09305 2398.2558
## 64 163.09305 2398.2558
## 65 163.09305 2398.2558
## 66 163.09305 2398.2558
## 67 163.09305 2398.2558
## 68 163.09305 2398.2558
## 69 163.09305 2398.2558
## 70 163.09305 2398.2558
## 71 163.09305 2398.2558
## 72 163.09305 2398.2558
## 73 182.03030 3174.4186
## 74 182.03030 3174.4186
## 75 182.03030 3174.4186
## 76 182.03030 3174.4186
## 77 182.03030 3174.4186
## 78 182.03030 3174.4186
## 79 182.03030 3174.4186
## 80 182.03030 3174.4186
## 81 182.03030 3174.4186
## 82 182.03030 3174.4186
## 83 182.03030 3174.4186
## 84 182.03030 3174.4186
## 85 182.03030 3174.4186
## 86 182.03030 3174.4186
## 87 182.03030 3174.4186
## 88 182.03030 3174.4186
## 89 182.03030 3174.4186
## 90 182.03030 3174.4186
## 91 182.03030 3174.4186
## 92 182.03030 3174.4186
## 93 182.03030 3174.4186
## 94 182.03030 3174.4186
## 95 182.03030 3174.4186
## 96 182.03030 3174.4186
## 97 140.28580 4006.3953
## 98 140.28580 4006.3953
## 99 140.28580 4006.3953
## 100 140.28580 4006.3953
## 101 140.28580 4006.3953
## 102 140.28580 4006.3953
## 103 140.28580 4006.3953
## 104 140.28580 4006.3953
## 105 140.28580 4006.3953
## 106 140.28580 4006.3953
## 107 140.28580 4006.3953
## 108 140.28580 4006.3953
## 109 140.28580 4006.3953
## 110 140.28580 4006.3953
## 111 140.28580 4006.3953
## 112 140.28580 4006.3953
## 113 140.28580 4006.3953
## 114 140.28580 4006.3953
## 115 140.28580 4006.3953
## 116 140.28580 4006.3953
## 117 140.28580 4006.3953
## 118 140.28580 4006.3953
## 119 140.28580 4006.3953
## 120 140.28580 4006.3953
## 121 117.28324 3750.0000
## 122 117.28324 3750.0000
## 123 117.28324 3750.0000
## 124 117.28324 3750.0000
## 125 117.28324 3750.0000
## 126 117.28324 3750.0000
## 127 117.28324 3750.0000
## 128 117.28324 3750.0000
## 129 117.28324 3750.0000
## 130 117.28324 3750.0000
## 131 117.28324 3750.0000
## 132 117.28324 3750.0000
## 133 117.28324 3750.0000
## 134 117.28324 3750.0000
## 135 117.28324 3750.0000
## 136 117.28324 3750.0000
## 137 117.28324 3750.0000
## 138 117.28324 3750.0000
## 139 117.28324 3750.0000
## 140 117.28324 3750.0000
## 141 117.28324 3750.0000
## 142 117.28324 3750.0000
## 143 117.28324 3750.0000
## 144 117.28324 3750.0000
## 145 103.80974 2986.9186
## 146 103.80974 2986.9186
## 147 103.80974 2986.9186
## 148 103.80974 2986.9186
## 149 103.80974 2986.9186
## 150 103.80974 2986.9186
## 151 103.80974 2986.9186
## 152 103.80974 2986.9186
## 153 103.80974 2986.9186
## 154 103.80974 2986.9186
## 155 103.80974 2986.9186
## 156 103.80974 2986.9186
## 157 103.80974 2986.9186
## 158 103.80974 2986.9186
## 159 103.80974 2986.9186
## 160 103.80974 2986.9186
## 161 103.80974 2986.9186
## 162 103.80974 2986.9186
## 163 103.80974 2986.9186
## 164 103.80974 2986.9186
## 165 103.80974 2986.9186
## 166 103.80974 2986.9186
## 167 103.80974 2986.9186
## 168 103.80974 2986.9186
## 169 92.60172 1748.5465
## 170 92.60172 1748.5465
## 171 92.60172 1748.5465
## 172 92.60172 1748.5465
## 173 92.60172 1748.5465
## 174 92.60172 1748.5465
## 175 92.60172 1748.5465
## 176 92.60172 1748.5465
## 177 92.60172 1748.5465
## 178 92.60172 1748.5465
## 179 92.60172 1748.5465
## 180 92.60172 1748.5465
## 181 92.60172 1748.5465
## 182 92.60172 1748.5465
## 183 92.60172 1748.5465
## 184 92.60172 1748.5465
## 185 92.60172 1748.5465
## 186 92.60172 1748.5465
## 187 92.60172 1748.5465
## 188 92.60172 1748.5465
## 189 92.60172 1748.5465
## 190 92.60172 1748.5465
## 191 92.60172 1748.5465
## 192 92.60172 1748.5465
## 193 96.92197 217.1512
## 194 96.92197 217.1512
## 195 96.92197 217.1512
## 196 96.92197 217.1512
## 197 96.92197 217.1512
## 198 96.92197 217.1512
## 199 96.92197 217.1512
## 200 96.92197 217.1512
## 201 96.92197 217.1512
## 202 96.92197 217.1512
## 203 96.92197 217.1512
## 204 96.92197 217.1512
## 205 96.92197 217.1512
## 206 96.92197 217.1512
## 207 96.92197 217.1512
## 208 96.92197 217.1512
## 209 96.92197 217.1512
## 210 96.92197 217.1512
## 211 96.92197 217.1512
## 212 96.92197 217.1512
## 213 96.92197 217.1512
## 214 96.92197 217.1512
## 215 96.92197 217.1512
## 216 96.92197 217.1512
## 217 229.78020 5537.7907
## 218 229.78020 5537.7907
## 219 229.78020 5537.7907
## 220 229.78020 5537.7907
## 221 229.78020 5537.7907
## 222 229.78020 5537.7907
## 223 229.78020 5537.7907
## 224 229.78020 5537.7907
## 225 229.78020 5537.7907
## 226 229.78020 5537.7907
## 227 229.78020 5537.7907
## 228 229.78020 5537.7907
## 229 229.78020 5537.7907
## 230 229.78020 5537.7907
## 231 229.78020 5537.7907
## 232 229.78020 5537.7907
## 233 229.78020 5537.7907
## 234 229.78020 5537.7907
## 235 229.78020 5537.7907
## 236 229.78020 5537.7907
## 237 229.78020 5537.7907
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## 1484 139.36826 7709.3023
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## 1488 139.36826 7709.3023
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## 1490 98.96723 3553.7791
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## 1498 98.96723 3553.7791
## 1499 98.96723 3553.7791
## 1500 98.96723 3553.7791
## 1501 98.96723 3553.7791
## 1502 98.96723 3553.7791
## 1503 98.96723 3553.7791
## 1504 98.96723 3553.7791
## 1505 98.96723 3553.7791
## 1506 98.96723 3553.7791
## 1507 98.96723 3553.7791
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## 1509 98.96723 3553.7791
## 1510 98.96723 3553.7791
## 1511 98.96723 3553.7791
## 1512 98.96723 3553.7791
## 1513 140.28302 2180.2326
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## 1525 140.28302 2180.2326
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## 1530 140.28302 2180.2326
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## 1534 140.28302 2180.2326
## 1535 140.28302 2180.2326
## 1536 140.28302 2180.2326
## 1537 96.92197 217.1512
## 1538 96.92197 217.1512
## 1539 96.92197 217.1512
## 1540 96.92197 217.1512
## 1541 96.92197 217.1512
## 1542 96.92197 217.1512
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## 1544 96.92197 217.1512
## 1545 96.92197 217.1512
## 1546 96.92197 217.1512
## 1547 96.92197 217.1512
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## 1549 96.92197 217.1512
## 1550 96.92197 217.1512
## 1551 96.92197 217.1512
## 1552 96.92197 217.1512
## 1553 96.92197 217.1512
## 1554 96.92197 217.1512
## 1555 96.92197 217.1512
## 1556 96.92197 217.1512
## 1557 96.92197 217.1512
## 1558 96.92197 217.1512
## 1559 96.92197 217.1512
## 1560 96.92197 217.1512
## 1561 110.30845 1090.1163
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## 1563 110.30845 1090.1163
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## 1569 110.30845 1090.1163
## 1570 110.30845 1090.1163
## 1571 110.30845 1090.1163
## 1572 110.30845 1090.1163
## 1573 110.30845 1090.1163
## 1574 110.30845 1090.1163
## 1575 110.30845 1090.1163
## 1576 110.30845 1090.1163
## 1577 110.30845 1090.1163
## 1578 110.30845 1090.1163
## 1579 110.30845 1090.1163
## 1580 110.30845 1090.1163
## 1581 110.30845 1090.1163
## 1582 110.30845 1090.1163
## 1583 110.30845 1090.1163
## 1584 110.30845 1090.1163
## 1585 177.73560 1796.5116
## 1586 177.73560 1796.5116
## 1587 177.73560 1796.5116
## 1588 177.73560 1796.5116
## 1589 177.73560 1796.5116
## 1590 177.73560 1796.5116
## 1591 177.73560 1796.5116
## 1592 177.73560 1796.5116
## 1593 177.73560 1796.5116
## 1594 177.73560 1796.5116
## 1595 177.73560 1796.5116
## 1596 177.73560 1796.5116
## 1597 177.73560 1796.5116
## 1598 177.73560 1796.5116
## 1599 177.73560 1796.5116
## 1600 177.73560 1796.5116
## 1601 177.73560 1796.5116
## 1602 177.73560 1796.5116
## 1603 177.73560 1796.5116
## 1604 177.73560 1796.5116
## 1605 177.73560 1796.5116
## 1606 177.73560 1796.5116
## 1607 177.73560 1796.5116
## 1608 177.73560 1796.5116
## 1609 114.02030 2616.2791
## 1610 114.02030 2616.2791
## 1611 114.02030 2616.2791
## 1612 114.02030 2616.2791
## 1613 114.02030 2616.2791
## 1614 114.02030 2616.2791
## 1615 114.02030 2616.2791
## 1616 114.02030 2616.2791
## 1617 114.02030 2616.2791
## 1618 114.02030 2616.2791
## 1619 114.02030 2616.2791
## 1620 114.02030 2616.2791
## 1621 114.02030 2616.2791
## 1622 114.02030 2616.2791
## 1623 114.02030 2616.2791
## 1624 114.02030 2616.2791
## 1625 114.02030 2616.2791
## 1626 114.02030 2616.2791
## 1627 114.02030 2616.2791
## 1628 114.02030 2616.2791
## 1629 114.02030 2616.2791
## 1630 114.02030 2616.2791
## 1631 114.02030 2616.2791
## 1632 114.02030 2616.2791
## 1633 146.27287 3680.2326
## 1634 146.27287 3680.2326
## 1635 146.27287 3680.2326
## 1636 146.27287 3680.2326
## 1637 146.27287 3680.2326
## 1638 146.27287 3680.2326
## 1639 146.27287 3680.2326
## 1640 146.27287 3680.2326
## 1641 146.27287 3680.2326
## 1642 146.27287 3680.2326
## 1643 146.27287 3680.2326
## 1644 146.27287 3680.2326
## 1645 146.27287 3680.2326
## 1646 146.27287 3680.2326
## 1647 146.27287 3680.2326
## 1648 146.27287 3680.2326
## 1649 146.27287 3680.2326
## 1650 146.27287 3680.2326
## 1651 146.27287 3680.2326
## 1652 146.27287 3680.2326
## 1653 146.27287 3680.2326
## 1654 146.27287 3680.2326
## 1655 146.27287 3680.2326
## 1656 146.27287 3680.2326
## 1657 59.54311 4360.4651
## 1658 59.54311 4360.4651
## 1659 59.54311 4360.4651
## 1660 59.54311 4360.4651
## 1661 59.54311 4360.4651
## 1662 59.54311 4360.4651
## 1663 59.54311 4360.4651
## 1664 59.54311 4360.4651
## 1665 59.54311 4360.4651
## 1666 59.54311 4360.4651
## 1667 59.54311 4360.4651
## 1668 59.54311 4360.4651
## 1669 59.54311 4360.4651
## 1670 59.54311 4360.4651
## 1671 59.54311 4360.4651
## 1672 59.54311 4360.4651
## 1673 59.54311 4360.4651
## 1674 59.54311 4360.4651
## 1675 59.54311 4360.4651
## 1676 59.54311 4360.4651
## 1677 59.54311 4360.4651
## 1678 59.54311 4360.4651
## 1679 59.54311 4360.4651
## 1680 59.54311 4360.4651
## 1681 39.67455 1090.1163
## 1682 39.67455 1090.1163
## 1683 39.67455 1090.1163
## 1684 39.67455 1090.1163
## 1685 39.67455 1090.1163
## 1686 39.67455 1090.1163
## 1687 39.67455 1090.1163
## 1688 39.67455 1090.1163
## 1689 39.67455 1090.1163
## 1690 39.67455 1090.1163
## 1691 39.67455 1090.1163
## 1692 39.67455 1090.1163
## 1693 39.67455 1090.1163
## 1694 39.67455 1090.1163
## 1695 39.67455 1090.1163
## 1696 39.67455 1090.1163
## 1697 39.67455 1090.1163
## 1698 39.67455 1090.1163
## 1699 39.67455 1090.1163
## 1700 39.67455 1090.1163
## 1701 39.67455 1090.1163
## 1702 39.67455 1090.1163
## 1703 39.67455 1090.1163
## 1704 39.67455 1090.1163
## 1705 31.70159 2180.2326
## 1706 31.70159 2180.2326
## 1707 31.70159 2180.2326
## 1708 31.70159 2180.2326
## 1709 31.70159 2180.2326
## 1710 31.70159 2180.2326
## 1711 31.70159 2180.2326
## 1712 31.70159 2180.2326
## 1713 31.70159 2180.2326
## 1714 31.70159 2180.2326
## 1715 31.70159 2180.2326
## 1716 31.70159 2180.2326
## 1717 31.70159 2180.2326
## 1718 31.70159 2180.2326
## 1719 31.70159 2180.2326
## 1720 31.70159 2180.2326
## 1721 31.70159 2180.2326
## 1722 31.70159 2180.2326
## 1723 31.70159 2180.2326
## 1724 31.70159 2180.2326
## 1725 31.70159 2180.2326
## 1726 31.70159 2180.2326
## 1727 31.70159 2180.2326
## 1728 31.70159 2180.2326
## 1729 96.92197 217.1512
## 1730 96.92197 217.1512
## 1731 96.92197 217.1512
## 1732 96.92197 217.1512
## 1733 96.92197 217.1512
## 1734 96.92197 217.1512
## 1735 96.92197 217.1512
## 1736 96.92197 217.1512
## 1737 96.92197 217.1512
## 1738 96.92197 217.1512
## 1739 96.92197 217.1512
## 1740 96.92197 217.1512
## 1741 96.92197 217.1512
## 1742 96.92197 217.1512
## 1743 96.92197 217.1512
## 1744 96.92197 217.1512
## 1745 96.92197 217.1512
## 1746 96.92197 217.1512
## 1747 96.92197 217.1512
## 1748 96.92197 217.1512
## 1749 96.92197 217.1512
## 1750 96.92197 217.1512
## 1751 96.92197 217.1512
## 1752 96.92197 217.1512
## 1753 115.04038 1090.1163
## 1754 115.04038 1090.1163
## 1755 115.04038 1090.1163
## 1756 115.04038 1090.1163
## 1757 115.04038 1090.1163
## 1758 115.04038 1090.1163
## 1759 115.04038 1090.1163
## 1760 115.04038 1090.1163
## 1761 115.04038 1090.1163
## 1762 115.04038 1090.1163
## 1763 115.04038 1090.1163
## 1764 115.04038 1090.1163
## 1765 115.04038 1090.1163
## 1766 115.04038 1090.1163
## 1767 115.04038 1090.1163
## 1768 115.04038 1090.1163
## 1769 115.04038 1090.1163
## 1770 115.04038 1090.1163
## 1771 115.04038 1090.1163
## 1772 115.04038 1090.1163
## 1773 115.04038 1090.1163
## 1774 115.04038 1090.1163
## 1775 115.04038 1090.1163
## 1776 115.04038 1090.1163
## 1777 170.95069 2886.6279
## 1778 170.95069 2886.6279
## 1779 170.95069 2886.6279
## 1780 170.95069 2886.6279
## 1781 170.95069 2886.6279
## 1782 170.95069 2886.6279
## 1783 170.95069 2886.6279
## 1784 170.95069 2886.6279
## 1785 170.95069 2886.6279
## 1786 170.95069 2886.6279
## 1787 170.95069 2886.6279
## 1788 170.95069 2886.6279
## 1789 170.95069 2886.6279
## 1790 170.95069 2886.6279
## 1791 170.95069 2886.6279
## 1792 170.95069 2886.6279
## 1793 170.95069 2886.6279
## 1794 170.95069 2886.6279
## 1795 170.95069 2886.6279
## 1796 170.95069 2886.6279
## 1797 170.95069 2886.6279
## 1798 170.95069 2886.6279
## 1799 170.95069 2886.6279
## 1800 170.95069 2886.6279
## 1801 207.04408 2616.2791
## 1802 207.04408 2616.2791
## 1803 207.04408 2616.2791
## 1804 207.04408 2616.2791
## 1805 207.04408 2616.2791
## 1806 207.04408 2616.2791
## 1807 207.04408 2616.2791
## 1808 207.04408 2616.2791
## 1809 207.04408 2616.2791
## 1810 207.04408 2616.2791
## 1811 207.04408 2616.2791
## 1812 207.04408 2616.2791
## 1813 207.04408 2616.2791
## 1814 207.04408 2616.2791
## 1815 207.04408 2616.2791
## 1816 207.04408 2616.2791
## 1817 207.04408 2616.2791
## 1818 207.04408 2616.2791
## 1819 207.04408 2616.2791
## 1820 207.04408 2616.2791
## 1821 207.04408 2616.2791
## 1822 207.04408 2616.2791
## 1823 207.04408 2616.2791
## 1824 207.04408 2616.2791
## 1825 204.76185 1090.1163
## 1826 204.76185 1090.1163
## 1827 204.76185 1090.1163
## 1828 204.76185 1090.1163
## 1829 204.76185 1090.1163
## 1830 204.76185 1090.1163
## 1831 204.76185 1090.1163
## 1832 204.76185 1090.1163
## 1833 204.76185 1090.1163
## 1834 204.76185 1090.1163
## 1835 204.76185 1090.1163
## 1836 204.76185 1090.1163
## 1837 204.76185 1090.1163
## 1838 204.76185 1090.1163
## 1839 204.76185 1090.1163
## 1840 204.76185 1090.1163
## 1841 204.76185 1090.1163
## 1842 204.76185 1090.1163
## 1843 204.76185 1090.1163
## 1844 204.76185 1090.1163
## 1845 204.76185 1090.1163
## 1846 204.76185 1090.1163
## 1847 204.76185 1090.1163
## 1848 204.76185 1090.1163
## 1849 170.47197 2162.7907
## 1850 170.47197 2162.7907
## 1851 170.47197 2162.7907
## 1852 170.47197 2162.7907
## 1853 170.47197 2162.7907
## 1854 170.47197 2162.7907
## 1855 170.47197 2162.7907
## 1856 170.47197 2162.7907
## 1857 170.47197 2162.7907
## 1858 170.47197 2162.7907
## 1859 170.47197 2162.7907
## 1860 170.47197 2162.7907
## 1861 170.47197 2162.7907
## 1862 170.47197 2162.7907
## 1863 170.47197 2162.7907
## 1864 170.47197 2162.7907
## 1865 170.47197 2162.7907
## 1866 170.47197 2162.7907
## 1867 170.47197 2162.7907
## 1868 170.47197 2162.7907
## 1869 170.47197 2162.7907
## 1870 170.47197 2162.7907
## 1871 170.47197 2162.7907
## 1872 170.47197 2162.7907
## 1873 149.52700 4329.9419
## 1874 149.52700 4329.9419
## 1875 149.52700 4329.9419
## 1876 149.52700 4329.9419
## 1877 149.52700 4329.9419
## 1878 149.52700 4329.9419
## 1879 149.52700 4329.9419
## 1880 149.52700 4329.9419
## 1881 149.52700 4329.9419
## 1882 149.52700 4329.9419
## 1883 149.52700 4329.9419
## 1884 149.52700 4329.9419
## 1885 149.52700 4329.9419
## 1886 149.52700 4329.9419
## 1887 149.52700 4329.9419
## 1888 149.52700 4329.9419
## 1889 149.52700 4329.9419
## 1890 149.52700 4329.9419
## 1891 149.52700 4329.9419
## 1892 149.52700 4329.9419
## 1893 149.52700 4329.9419
## 1894 149.52700 4329.9419
## 1895 149.52700 4329.9419
## 1896 149.52700 4329.9419
## 1897 110.26880 3920.0581
## 1898 110.26880 3920.0581
## 1899 110.26880 3920.0581
## 1900 110.26880 3920.0581
## 1901 110.26880 3920.0581
## 1902 110.26880 3920.0581
## 1903 110.26880 3920.0581
## 1904 110.26880 3920.0581
## 1905 110.26880 3920.0581
## 1906 110.26880 3920.0581
## 1907 110.26880 3920.0581
## 1908 110.26880 3920.0581
## 1909 110.26880 3920.0581
## 1910 110.26880 3920.0581
## 1911 110.26880 3920.0581
## 1912 110.26880 3920.0581
## 1913 110.26880 3920.0581
## 1914 110.26880 3920.0581
## 1915 110.26880 3920.0581
## 1916 110.26880 3920.0581
## 1917 110.26880 3920.0581
## 1918 110.26880 3920.0581
## 1919 110.26880 3920.0581
## 1920 110.26880 3920.0581
## 1921 96.92197 217.1512
## 1922 96.92197 217.1512
## 1923 96.92197 217.1512
## 1924 96.92197 217.1512
## 1925 96.92197 217.1512
## 1926 96.92197 217.1512
## 1927 96.92197 217.1512
## 1928 96.92197 217.1512
## 1929 96.92197 217.1512
## 1930 96.92197 217.1512
## 1931 96.92197 217.1512
## 1932 96.92197 217.1512
## 1933 96.92197 217.1512
## 1934 96.92197 217.1512
## 1935 96.92197 217.1512
## 1936 96.92197 217.1512
## 1937 96.92197 217.1512
## 1938 96.92197 217.1512
## 1939 96.92197 217.1512
## 1940 96.92197 217.1512
## 1941 96.92197 217.1512
## 1942 96.92197 217.1512
## 1943 96.92197 217.1512
## 1944 96.92197 217.1512
## 1945 151.39694 1473.8372
## 1946 151.39694 1473.8372
## 1947 151.39694 1473.8372
## 1948 151.39694 1473.8372
## 1949 151.39694 1473.8372
## 1950 151.39694 1473.8372
## 1951 151.39694 1473.8372
## 1952 151.39694 1473.8372
## 1953 151.39694 1473.8372
## 1954 151.39694 1473.8372
## 1955 151.39694 1473.8372
## 1956 151.39694 1473.8372
## 1957 151.39694 1473.8372
## 1958 151.39694 1473.8372
## 1959 151.39694 1473.8372
## 1960 151.39694 1473.8372
## 1961 151.39694 1473.8372
## 1962 151.39694 1473.8372
## 1963 151.39694 1473.8372
## 1964 151.39694 1473.8372
## 1965 151.39694 1473.8372
## 1966 151.39694 1473.8372
## 1967 151.39694 1473.8372
## 1968 151.39694 1473.8372
## 1969 158.17674 2502.9070
## 1970 158.17674 2502.9070
## 1971 158.17674 2502.9070
## 1972 158.17674 2502.9070
## 1973 158.17674 2502.9070
## 1974 158.17674 2502.9070
## 1975 158.17674 2502.9070
## 1976 158.17674 2502.9070
## 1977 158.17674 2502.9070
## 1978 158.17674 2502.9070
## 1979 158.17674 2502.9070
## 1980 158.17674 2502.9070
## 1981 158.17674 2502.9070
## 1982 158.17674 2502.9070
## 1983 158.17674 2502.9070
## 1984 158.17674 2502.9070
## 1985 158.17674 2502.9070
## 1986 158.17674 2502.9070
## 1987 158.17674 2502.9070
## 1988 158.17674 2502.9070
## 1989 158.17674 2502.9070
## 1990 158.17674 2502.9070
## 1991 158.17674 2502.9070
## 1992 158.17674 2502.9070
## 1993 196.58628 3388.9535
## 1994 196.58628 3388.9535
## 1995 196.58628 3388.9535
## 1996 196.58628 3388.9535
## 1997 196.58628 3388.9535
## 1998 196.58628 3388.9535
## 1999 196.58628 3388.9535
## 2000 196.58628 3388.9535
## 2001 196.58628 3388.9535
## 2002 196.58628 3388.9535
## 2003 196.58628 3388.9535
## 2004 196.58628 3388.9535
## 2005 196.58628 3388.9535
## 2006 196.58628 3388.9535
## 2007 196.58628 3388.9535
## 2008 196.58628 3388.9535
## 2009 196.58628 3388.9535
## 2010 196.58628 3388.9535
## 2011 196.58628 3388.9535
## 2012 196.58628 3388.9535
## 2013 196.58628 3388.9535
## 2014 196.58628 3388.9535
## 2015 196.58628 3388.9535
## 2016 196.58628 3388.9535
## 2017 136.02956 3584.3023
## 2018 136.02956 3584.3023
## 2019 136.02956 3584.3023
## 2020 136.02956 3584.3023
## 2021 136.02956 3584.3023
## 2022 136.02956 3584.3023
## 2023 136.02956 3584.3023
## 2024 136.02956 3584.3023
## 2025 136.02956 3584.3023
## 2026 136.02956 3584.3023
## 2027 136.02956 3584.3023
## 2028 136.02956 3584.3023
## 2029 136.02956 3584.3023
## 2030 136.02956 3584.3023
## 2031 136.02956 3584.3023
## 2032 136.02956 3584.3023
## 2033 136.02956 3584.3023
## 2034 136.02956 3584.3023
## 2035 136.02956 3584.3023
## 2036 136.02956 3584.3023
## 2037 136.02956 3584.3023
## 2038 136.02956 3584.3023
## 2039 136.02956 3584.3023
## 2040 136.02956 3584.3023
## 2041 116.45107 3750.0000
## 2042 116.45107 3750.0000
## 2043 116.45107 3750.0000
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## 3221 103.92120 4765.9884
## 3222 103.92120 4765.9884
## 3223 103.92120 4765.9884
## 3224 103.92120 4765.9884
## 3225 103.92120 4765.9884
## total_metaecosystem_Cep_indiv total_metaecosystem_Col_indiv
## 1 1794.767 4812.20930
## 2 1794.767 4812.20930
## 3 1794.767 4812.20930
## 4 1794.767 4812.20930
## 5 1794.767 4812.20930
## 6 1794.767 4812.20930
## 7 1794.767 4812.20930
## 8 1794.767 4812.20930
## 9 1794.767 4812.20930
## 10 1794.767 4812.20930
## 11 1794.767 4812.20930
## 12 1794.767 4812.20930
## 13 1794.767 4812.20930
## 14 1794.767 4812.20930
## 15 1794.767 4812.20930
## 16 1794.767 4812.20930
## 17 1794.767 4812.20930
## 18 1794.767 4812.20930
## 19 1794.767 4812.20930
## 20 1794.767 4812.20930
## 21 1794.767 4812.20930
## 22 1794.767 4812.20930
## 23 1794.767 4812.20930
## 24 1794.767 4812.20930
## 25 8056.395 20305.81395
## 26 8056.395 20305.81395
## 27 8056.395 20305.81395
## 28 8056.395 20305.81395
## 29 8056.395 20305.81395
## 30 8056.395 20305.81395
## 31 8056.395 20305.81395
## 32 8056.395 20305.81395
## 33 8056.395 20305.81395
## 34 8056.395 20305.81395
## 35 8056.395 20305.81395
## 36 8056.395 20305.81395
## 37 8056.395 20305.81395
## 38 8056.395 20305.81395
## 39 8056.395 20305.81395
## 40 8056.395 20305.81395
## 41 8056.395 20305.81395
## 42 8056.395 20305.81395
## 43 8056.395 20305.81395
## 44 8056.395 20305.81395
## 45 8056.395 20305.81395
## 46 8056.395 20305.81395
## 47 8056.395 20305.81395
## 48 8056.395 20305.81395
## 49 11759.302 16356.97674
## 50 11759.302 16356.97674
## 51 11759.302 16356.97674
## 52 11759.302 16356.97674
## 53 11759.302 16356.97674
## 54 11759.302 16356.97674
## 55 11759.302 16356.97674
## 56 11759.302 16356.97674
## 57 11759.302 16356.97674
## 58 11759.302 16356.97674
## 59 11759.302 16356.97674
## 60 11759.302 16356.97674
## 61 11759.302 16356.97674
## 62 11759.302 16356.97674
## 63 11759.302 16356.97674
## 64 11759.302 16356.97674
## 65 11759.302 16356.97674
## 66 11759.302 16356.97674
## 67 11759.302 16356.97674
## 68 11759.302 16356.97674
## 69 11759.302 16356.97674
## 70 11759.302 16356.97674
## 71 11759.302 16356.97674
## 72 11759.302 16356.97674
## 73 6552.907 6973.25581
## 74 6552.907 6973.25581
## 75 6552.907 6973.25581
## 76 6552.907 6973.25581
## 77 6552.907 6973.25581
## 78 6552.907 6973.25581
## 79 6552.907 6973.25581
## 80 6552.907 6973.25581
## 81 6552.907 6973.25581
## 82 6552.907 6973.25581
## 83 6552.907 6973.25581
## 84 6552.907 6973.25581
## 85 6552.907 6973.25581
## 86 6552.907 6973.25581
## 87 6552.907 6973.25581
## 88 6552.907 6973.25581
## 89 6552.907 6973.25581
## 90 6552.907 6973.25581
## 91 6552.907 6973.25581
## 92 6552.907 6973.25581
## 93 6552.907 6973.25581
## 94 6552.907 6973.25581
## 95 6552.907 6973.25581
## 96 6552.907 6973.25581
## 97 9326.163 3912.20930
## 98 9326.163 3912.20930
## 99 9326.163 3912.20930
## 100 9326.163 3912.20930
## 101 9326.163 3912.20930
## 102 9326.163 3912.20930
## 103 9326.163 3912.20930
## 104 9326.163 3912.20930
## 105 9326.163 3912.20930
## 106 9326.163 3912.20930
## 107 9326.163 3912.20930
## 108 9326.163 3912.20930
## 109 9326.163 3912.20930
## 110 9326.163 3912.20930
## 111 9326.163 3912.20930
## 112 9326.163 3912.20930
## 113 9326.163 3912.20930
## 114 9326.163 3912.20930
## 115 9326.163 3912.20930
## 116 9326.163 3912.20930
## 117 9326.163 3912.20930
## 118 9326.163 3912.20930
## 119 9326.163 3912.20930
## 120 9326.163 3912.20930
## 121 9818.023 0.00000
## 122 9818.023 0.00000
## 123 9818.023 0.00000
## 124 9818.023 0.00000
## 125 9818.023 0.00000
## 126 9818.023 0.00000
## 127 9818.023 0.00000
## 128 9818.023 0.00000
## 129 9818.023 0.00000
## 130 9818.023 0.00000
## 131 9818.023 0.00000
## 132 9818.023 0.00000
## 133 9818.023 0.00000
## 134 9818.023 0.00000
## 135 9818.023 0.00000
## 136 9818.023 0.00000
## 137 9818.023 0.00000
## 138 9818.023 0.00000
## 139 9818.023 0.00000
## 140 9818.023 0.00000
## 141 9818.023 0.00000
## 142 9818.023 0.00000
## 143 9818.023 0.00000
## 144 9818.023 0.00000
## 145 15800.581 418.60465
## 146 15800.581 418.60465
## 147 15800.581 418.60465
## 148 15800.581 418.60465
## 149 15800.581 418.60465
## 150 15800.581 418.60465
## 151 15800.581 418.60465
## 152 15800.581 418.60465
## 153 15800.581 418.60465
## 154 15800.581 418.60465
## 155 15800.581 418.60465
## 156 15800.581 418.60465
## 157 15800.581 418.60465
## 158 15800.581 418.60465
## 159 15800.581 418.60465
## 160 15800.581 418.60465
## 161 15800.581 418.60465
## 162 15800.581 418.60465
## 163 15800.581 418.60465
## 164 15800.581 418.60465
## 165 15800.581 418.60465
## 166 15800.581 418.60465
## 167 15800.581 418.60465
## 168 15800.581 418.60465
## 169 15422.093 0.00000
## 170 15422.093 0.00000
## 171 15422.093 0.00000
## 172 15422.093 0.00000
## 173 15422.093 0.00000
## 174 15422.093 0.00000
## 175 15422.093 0.00000
## 176 15422.093 0.00000
## 177 15422.093 0.00000
## 178 15422.093 0.00000
## 179 15422.093 0.00000
## 180 15422.093 0.00000
## 181 15422.093 0.00000
## 182 15422.093 0.00000
## 183 15422.093 0.00000
## 184 15422.093 0.00000
## 185 15422.093 0.00000
## 186 15422.093 0.00000
## 187 15422.093 0.00000
## 188 15422.093 0.00000
## 189 15422.093 0.00000
## 190 15422.093 0.00000
## 191 15422.093 0.00000
## 192 15422.093 0.00000
## 193 1794.767 4812.20930
## 194 1794.767 4812.20930
## 195 1794.767 4812.20930
## 196 1794.767 4812.20930
## 197 1794.767 4812.20930
## 198 1794.767 4812.20930
## 199 1794.767 4812.20930
## 200 1794.767 4812.20930
## 201 1794.767 4812.20930
## 202 1794.767 4812.20930
## 203 1794.767 4812.20930
## 204 1794.767 4812.20930
## 205 1794.767 4812.20930
## 206 1794.767 4812.20930
## 207 1794.767 4812.20930
## 208 1794.767 4812.20930
## 209 1794.767 4812.20930
## 210 1794.767 4812.20930
## 211 1794.767 4812.20930
## 212 1794.767 4812.20930
## 213 1794.767 4812.20930
## 214 1794.767 4812.20930
## 215 1794.767 4812.20930
## 216 1794.767 4812.20930
## 217 9695.930 12404.65116
## 218 9695.930 12404.65116
## 219 9695.930 12404.65116
## 220 9695.930 12404.65116
## 221 9695.930 12404.65116
## 222 9695.930 12404.65116
## 223 9695.930 12404.65116
## 224 9695.930 12404.65116
## 225 9695.930 12404.65116
## 226 9695.930 12404.65116
## 227 9695.930 12404.65116
## 228 9695.930 12404.65116
## 229 9695.930 12404.65116
## 230 9695.930 12404.65116
## 231 9695.930 12404.65116
## 232 9695.930 12404.65116
## 233 9695.930 12404.65116
## 234 9695.930 12404.65116
## 235 9695.930 12404.65116
## 236 9695.930 12404.65116
## 237 9695.930 12404.65116
## 238 9695.930 12404.65116
## 239 9695.930 12404.65116
## 240 9695.930 12404.65116
## 241 12648.837 5159.30233
## 242 12648.837 5159.30233
## 243 12648.837 5159.30233
## 244 12648.837 5159.30233
## 245 12648.837 5159.30233
## 246 12648.837 5159.30233
## 247 12648.837 5159.30233
## 248 12648.837 5159.30233
## 249 12648.837 5159.30233
## 250 12648.837 5159.30233
## 251 12648.837 5159.30233
## 252 12648.837 5159.30233
## 253 12648.837 5159.30233
## 254 12648.837 5159.30233
## 255 12648.837 5159.30233
## 256 12648.837 5159.30233
## 257 12648.837 5159.30233
## 258 12648.837 5159.30233
## 259 12648.837 5159.30233
## 260 12648.837 5159.30233
## 261 12648.837 5159.30233
## 262 12648.837 5159.30233
## 263 12648.837 5159.30233
## 264 12648.837 5159.30233
## 265 14872.674 3685.46512
## 266 14872.674 3685.46512
## 267 14872.674 3685.46512
## 268 14872.674 3685.46512
## 269 14872.674 3685.46512
## 270 14872.674 3685.46512
## 271 14872.674 3685.46512
## 272 14872.674 3685.46512
## 273 14872.674 3685.46512
## 274 14872.674 3685.46512
## 275 14872.674 3685.46512
## 276 14872.674 3685.46512
## 277 14872.674 3685.46512
## 278 14872.674 3685.46512
## 279 14872.674 3685.46512
## 280 14872.674 3685.46512
## 281 14872.674 3685.46512
## 282 14872.674 3685.46512
## 283 14872.674 3685.46512
## 284 14872.674 3685.46512
## 285 14872.674 3685.46512
## 286 14872.674 3685.46512
## 287 14872.674 3685.46512
## 288 14872.674 3685.46512
## 289 13006.395 397.67442
## 290 13006.395 397.67442
## 291 13006.395 397.67442
## 292 13006.395 397.67442
## 293 13006.395 397.67442
## 294 13006.395 397.67442
## 295 13006.395 397.67442
## 296 13006.395 397.67442
## 297 13006.395 397.67442
## 298 13006.395 397.67442
## 299 13006.395 397.67442
## 300 13006.395 397.67442
## 301 13006.395 397.67442
## 302 13006.395 397.67442
## 303 13006.395 397.67442
## 304 13006.395 397.67442
## 305 13006.395 397.67442
## 306 13006.395 397.67442
## 307 13006.395 397.67442
## 308 13006.395 397.67442
## 309 13006.395 397.67442
## 310 13006.395 397.67442
## 311 13006.395 397.67442
## 312 13006.395 397.67442
## 313 8248.256 837.20930
## 314 8248.256 837.20930
## 315 8248.256 837.20930
## 316 8248.256 837.20930
## 317 8248.256 837.20930
## 318 8248.256 837.20930
## 319 8248.256 837.20930
## 320 8248.256 837.20930
## 321 8248.256 837.20930
## 322 8248.256 837.20930
## 323 8248.256 837.20930
## 324 8248.256 837.20930
## 325 8248.256 837.20930
## 326 8248.256 837.20930
## 327 8248.256 837.20930
## 328 8248.256 837.20930
## 329 8248.256 837.20930
## 330 8248.256 837.20930
## 331 8248.256 837.20930
## 332 8248.256 837.20930
## 333 8248.256 837.20930
## 334 8248.256 837.20930
## 335 8248.256 837.20930
## 336 8248.256 837.20930
## 337 12547.674 0.00000
## 338 12547.674 0.00000
## 339 12547.674 0.00000
## 340 12547.674 0.00000
## 341 12547.674 0.00000
## 342 12547.674 0.00000
## 343 12547.674 0.00000
## 344 12547.674 0.00000
## 345 12547.674 0.00000
## 346 12547.674 0.00000
## 347 12547.674 0.00000
## 348 12547.674 0.00000
## 349 12547.674 0.00000
## 350 12547.674 0.00000
## 351 12547.674 0.00000
## 352 12547.674 0.00000
## 353 12547.674 0.00000
## 354 12547.674 0.00000
## 355 12547.674 0.00000
## 356 12547.674 0.00000
## 357 12547.674 0.00000
## 358 12547.674 0.00000
## 359 12547.674 0.00000
## 360 12547.674 0.00000
## 361 10438.081 0.00000
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## 363 10438.081 0.00000
## 364 10438.081 0.00000
## 365 10438.081 0.00000
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## 374 10438.081 0.00000
## 375 10438.081 0.00000
## 376 10438.081 0.00000
## 377 10438.081 0.00000
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## 380 10438.081 0.00000
## 381 10438.081 0.00000
## 382 10438.081 0.00000
## 383 10438.081 0.00000
## 384 10438.081 0.00000
## 385 1794.767 4812.20930
## 386 1794.767 4812.20930
## 387 1794.767 4812.20930
## 388 1794.767 4812.20930
## 389 1794.767 4812.20930
## 390 1794.767 4812.20930
## 391 1794.767 4812.20930
## 392 1794.767 4812.20930
## 393 1794.767 4812.20930
## 394 1794.767 4812.20930
## 395 1794.767 4812.20930
## 396 1794.767 4812.20930
## 397 1794.767 4812.20930
## 398 1794.767 4812.20930
## 399 1794.767 4812.20930
## 400 1794.767 4812.20930
## 401 1794.767 4812.20930
## 402 1794.767 4812.20930
## 403 1794.767 4812.20930
## 404 1794.767 4812.20930
## 405 1794.767 4812.20930
## 406 1794.767 4812.20930
## 407 1794.767 4812.20930
## 408 1794.767 4812.20930
## 409 8344.186 17236.04651
## 410 8344.186 17236.04651
## 411 8344.186 17236.04651
## 412 8344.186 17236.04651
## 413 8344.186 17236.04651
## 414 8344.186 17236.04651
## 415 8344.186 17236.04651
## 416 8344.186 17236.04651
## 417 8344.186 17236.04651
## 418 8344.186 17236.04651
## 419 8344.186 17236.04651
## 420 8344.186 17236.04651
## 421 8344.186 17236.04651
## 422 8344.186 17236.04651
## 423 8344.186 17236.04651
## 424 8344.186 17236.04651
## 425 8344.186 17236.04651
## 426 8344.186 17236.04651
## 427 8344.186 17236.04651
## 428 8344.186 17236.04651
## 429 8344.186 17236.04651
## 430 8344.186 17236.04651
## 431 8344.186 17236.04651
## 432 8344.186 17236.04651
## 433 12762.209 6022.67442
## 434 12762.209 6022.67442
## 435 12762.209 6022.67442
## 436 12762.209 6022.67442
## 437 12762.209 6022.67442
## 438 12762.209 6022.67442
## 439 12762.209 6022.67442
## 440 12762.209 6022.67442
## 441 12762.209 6022.67442
## 442 12762.209 6022.67442
## 443 12762.209 6022.67442
## 444 12762.209 6022.67442
## 445 12762.209 6022.67442
## 446 12762.209 6022.67442
## 447 12762.209 6022.67442
## 448 12762.209 6022.67442
## 449 12762.209 6022.67442
## 450 12762.209 6022.67442
## 451 12762.209 6022.67442
## 452 12762.209 6022.67442
## 453 12762.209 6022.67442
## 454 12762.209 6022.67442
## 455 12762.209 6022.67442
## 456 12762.209 6022.67442
## 457 6770.930 4060.46512
## 458 6770.930 4060.46512
## 459 6770.930 4060.46512
## 460 6770.930 4060.46512
## 461 6770.930 4060.46512
## 462 6770.930 4060.46512
## 463 6770.930 4060.46512
## 464 6770.930 4060.46512
## 465 6770.930 4060.46512
## 466 6770.930 4060.46512
## 467 6770.930 4060.46512
## 468 6770.930 4060.46512
## 469 6770.930 4060.46512
## 470 6770.930 4060.46512
## 471 6770.930 4060.46512
## 472 6770.930 4060.46512
## 473 6770.930 4060.46512
## 474 6770.930 4060.46512
## 475 6770.930 4060.46512
## 476 6770.930 4060.46512
## 477 6770.930 4060.46512
## 478 6770.930 4060.46512
## 479 6770.930 4060.46512
## 480 6770.930 4060.46512
## 481 10730.233 397.67442
## 482 10730.233 397.67442
## 483 10730.233 397.67442
## 484 10730.233 397.67442
## 485 10730.233 397.67442
## 486 10730.233 397.67442
## 487 10730.233 397.67442
## 488 10730.233 397.67442
## 489 10730.233 397.67442
## 490 10730.233 397.67442
## 491 10730.233 397.67442
## 492 10730.233 397.67442
## 493 10730.233 397.67442
## 494 10730.233 397.67442
## 495 10730.233 397.67442
## 496 10730.233 397.67442
## 497 10730.233 397.67442
## 498 10730.233 397.67442
## 499 10730.233 397.67442
## 500 10730.233 397.67442
## 501 10730.233 397.67442
## 502 10730.233 397.67442
## 503 10730.233 397.67442
## 504 10730.233 397.67442
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## 535 13943.023 327.03488
## 536 13943.023 327.03488
## 537 13943.023 327.03488
## 538 13943.023 327.03488
## 539 13943.023 327.03488
## 540 13943.023 327.03488
## 541 13943.023 327.03488
## 542 13943.023 327.03488
## 543 13943.023 327.03488
## 544 13943.023 327.03488
## 545 13943.023 327.03488
## 546 13943.023 327.03488
## 547 13943.023 327.03488
## 548 13943.023 327.03488
## 549 13943.023 327.03488
## 550 13943.023 327.03488
## 551 13943.023 327.03488
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## 578 1794.767 4812.20930
## 579 1794.767 4812.20930
## 580 1794.767 4812.20930
## 581 1794.767 4812.20930
## 582 1794.767 4812.20930
## 583 1794.767 4812.20930
## 584 1794.767 4812.20930
## 585 1794.767 4812.20930
## 586 1794.767 4812.20930
## 587 1794.767 4812.20930
## 588 1794.767 4812.20930
## 589 1794.767 4812.20930
## 590 1794.767 4812.20930
## 591 1794.767 4812.20930
## 592 1794.767 4812.20930
## 593 1794.767 4812.20930
## 594 1794.767 4812.20930
## 595 1794.767 4812.20930
## 596 1794.767 4812.20930
## 597 1794.767 4812.20930
## 598 1794.767 4812.20930
## 599 1794.767 4812.20930
## 600 1794.767 4812.20930
## 601 10227.907 11567.44186
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## 603 10227.907 11567.44186
## 604 10227.907 11567.44186
## 605 10227.907 11567.44186
## 606 10227.907 11567.44186
## 607 10227.907 11567.44186
## 608 10227.907 11567.44186
## 609 10227.907 11567.44186
## 610 10227.907 11567.44186
## 611 10227.907 11567.44186
## 612 10227.907 11567.44186
## 613 10227.907 11567.44186
## 614 10227.907 11567.44186
## 615 10227.907 11567.44186
## 616 10227.907 11567.44186
## 617 10227.907 11567.44186
## 618 10227.907 11567.44186
## 619 10227.907 11567.44186
## 620 10227.907 11567.44186
## 621 10227.907 11567.44186
## 622 10227.907 11567.44186
## 623 10227.907 11567.44186
## 624 10227.907 11567.44186
## 625 17148.837 2072.09302
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## 628 17148.837 2072.09302
## 629 17148.837 2072.09302
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## 634 17148.837 2072.09302
## 635 17148.837 2072.09302
## 636 17148.837 2072.09302
## 637 17148.837 2072.09302
## 638 17148.837 2072.09302
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## 2018 9950.581 3514.53488
## 2019 9950.581 3514.53488
## 2020 9950.581 3514.53488
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## 2028 9950.581 3514.53488
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## 3096 1794.767 4812.20930
## 3097 8750.581 13489.53488
## 3098 8750.581 13489.53488
## 3099 8750.581 13489.53488
## 3100 8750.581 13489.53488
## 3101 8750.581 13489.53488
## 3102 8750.581 13489.53488
## 3103 8750.581 13489.53488
## 3104 8750.581 13489.53488
## 3105 8750.581 13489.53488
## 3106 8750.581 13489.53488
## 3107 8750.581 13489.53488
## 3108 8750.581 13489.53488
## 3109 8750.581 13489.53488
## 3110 8750.581 13489.53488
## 3111 8750.581 13489.53488
## 3112 8750.581 13489.53488
## 3113 8750.581 13489.53488
## 3114 8750.581 13489.53488
## 3115 8750.581 13489.53488
## 3116 8750.581 13489.53488
## 3117 8750.581 13489.53488
## 3118 8750.581 13489.53488
## 3119 8750.581 13489.53488
## 3120 8750.581 13489.53488
## 3121 12280.814 5752.32558
## 3122 12280.814 5752.32558
## 3123 12280.814 5752.32558
## 3124 12280.814 5752.32558
## 3125 12280.814 5752.32558
## 3126 12280.814 5752.32558
## 3127 12280.814 5752.32558
## 3128 12280.814 5752.32558
## 3129 12280.814 5752.32558
## 3130 12280.814 5752.32558
## 3131 12280.814 5752.32558
## 3132 12280.814 5752.32558
## 3133 12280.814 5752.32558
## 3134 12280.814 5752.32558
## 3135 12280.814 5752.32558
## 3136 12280.814 5752.32558
## 3137 12280.814 5752.32558
## 3138 12280.814 5752.32558
## 3139 12280.814 5752.32558
## 3140 12280.814 5752.32558
## 3141 12280.814 5752.32558
## 3142 12280.814 5752.32558
## 3143 12280.814 5752.32558
## 3144 12280.814 5752.32558
## 3145 15790.116 4046.51163
## 3146 15790.116 4046.51163
## 3147 15790.116 4046.51163
## 3148 15790.116 4046.51163
## 3149 15790.116 4046.51163
## 3150 15790.116 4046.51163
## 3151 15790.116 4046.51163
## 3152 15790.116 4046.51163
## 3153 15790.116 4046.51163
## 3154 15790.116 4046.51163
## 3155 15790.116 4046.51163
## 3156 15790.116 4046.51163
## 3157 15790.116 4046.51163
## 3158 15790.116 4046.51163
## 3159 15790.116 4046.51163
## 3160 15790.116 4046.51163
## 3161 15790.116 4046.51163
## 3162 15790.116 4046.51163
## 3163 15790.116 4046.51163
## 3164 15790.116 4046.51163
## 3165 15790.116 4046.51163
## 3166 15790.116 4046.51163
## 3167 15790.116 4046.51163
## 3168 15790.116 4046.51163
## 3169 12861.628 568.60465
## 3170 12861.628 568.60465
## 3171 12861.628 568.60465
## 3172 12861.628 568.60465
## 3173 12861.628 568.60465
## 3174 12861.628 568.60465
## 3175 12861.628 568.60465
## 3176 12861.628 568.60465
## 3177 12861.628 568.60465
## 3178 12861.628 568.60465
## 3179 12861.628 568.60465
## 3180 12861.628 568.60465
## 3181 12861.628 568.60465
## 3182 12861.628 568.60465
## 3183 12861.628 568.60465
## 3184 12861.628 568.60465
## 3185 12861.628 568.60465
## 3186 12861.628 568.60465
## 3187 12861.628 568.60465
## 3188 12861.628 568.60465
## 3189 12861.628 568.60465
## 3190 12861.628 568.60465
## 3191 12861.628 568.60465
## 3192 12861.628 568.60465
## 3193 7986.628 837.20930
## 3194 7986.628 837.20930
## 3195 7986.628 837.20930
## 3196 7986.628 837.20930
## 3197 7986.628 837.20930
## 3198 7986.628 837.20930
## 3199 7986.628 837.20930
## 3200 7986.628 837.20930
## 3201 7986.628 837.20930
## 3202 7986.628 837.20930
## 3203 7986.628 837.20930
## 3204 7986.628 837.20930
## 3205 7986.628 837.20930
## 3206 7986.628 837.20930
## 3207 7986.628 837.20930
## 3208 7986.628 837.20930
## 3209 7986.628 837.20930
## 3210 7986.628 837.20930
## 3211 7986.628 837.20930
## 3212 7986.628 837.20930
## 3213 7986.628 837.20930
## 3214 7986.628 837.20930
## 3215 7986.628 837.20930
## 3216 7986.628 837.20930
## 3217 12342.733 0.00000
## 3218 12342.733 0.00000
## 3219 12342.733 0.00000
## 3220 12342.733 0.00000
## 3221 12342.733 0.00000
## 3222 12342.733 0.00000
## 3223 12342.733 0.00000
## 3224 12342.733 0.00000
## 3225 12342.733 0.00000
## total_metaecosystem_Eug_indiv total_metaecosystem_Eup_indiv
## 1 7834.88372 3155.2326
## 2 7834.88372 3155.2326
## 3 7834.88372 3155.2326
## 4 7834.88372 3155.2326
## 5 7834.88372 3155.2326
## 6 7834.88372 3155.2326
## 7 7834.88372 3155.2326
## 8 7834.88372 3155.2326
## 9 7834.88372 3155.2326
## 10 7834.88372 3155.2326
## 11 7834.88372 3155.2326
## 12 7834.88372 3155.2326
## 13 7834.88372 3155.2326
## 14 7834.88372 3155.2326
## 15 7834.88372 3155.2326
## 16 7834.88372 3155.2326
## 17 7834.88372 3155.2326
## 18 7834.88372 3155.2326
## 19 7834.88372 3155.2326
## 20 7834.88372 3155.2326
## 21 7834.88372 3155.2326
## 22 7834.88372 3155.2326
## 23 7834.88372 3155.2326
## 24 7834.88372 3155.2326
## 25 3057.55814 4484.3023
## 26 3057.55814 4484.3023
## 27 3057.55814 4484.3023
## 28 3057.55814 4484.3023
## 29 3057.55814 4484.3023
## 30 3057.55814 4484.3023
## 31 3057.55814 4484.3023
## 32 3057.55814 4484.3023
## 33 3057.55814 4484.3023
## 34 3057.55814 4484.3023
## 35 3057.55814 4484.3023
## 36 3057.55814 4484.3023
## 37 3057.55814 4484.3023
## 38 3057.55814 4484.3023
## 39 3057.55814 4484.3023
## 40 3057.55814 4484.3023
## 41 3057.55814 4484.3023
## 42 3057.55814 4484.3023
## 43 3057.55814 4484.3023
## 44 3057.55814 4484.3023
## 45 3057.55814 4484.3023
## 46 3057.55814 4484.3023
## 47 3057.55814 4484.3023
## 48 3057.55814 4484.3023
## 49 218.02326 1318.6047
## 50 218.02326 1318.6047
## 51 218.02326 1318.6047
## 52 218.02326 1318.6047
## 53 218.02326 1318.6047
## 54 218.02326 1318.6047
## 55 218.02326 1318.6047
## 56 218.02326 1318.6047
## 57 218.02326 1318.6047
## 58 218.02326 1318.6047
## 59 218.02326 1318.6047
## 60 218.02326 1318.6047
## 61 218.02326 1318.6047
## 62 218.02326 1318.6047
## 63 218.02326 1318.6047
## 64 218.02326 1318.6047
## 65 218.02326 1318.6047
## 66 218.02326 1318.6047
## 67 218.02326 1318.6047
## 68 218.02326 1318.6047
## 69 218.02326 1318.6047
## 70 218.02326 1318.6047
## 71 218.02326 1318.6047
## 72 218.02326 1318.6047
## 73 0.00000 5084.3023
## 74 0.00000 5084.3023
## 75 0.00000 5084.3023
## 76 0.00000 5084.3023
## 77 0.00000 5084.3023
## 78 0.00000 5084.3023
## 79 0.00000 5084.3023
## 80 0.00000 5084.3023
## 81 0.00000 5084.3023
## 82 0.00000 5084.3023
## 83 0.00000 5084.3023
## 84 0.00000 5084.3023
## 85 0.00000 5084.3023
## 86 0.00000 5084.3023
## 87 0.00000 5084.3023
## 88 0.00000 5084.3023
## 89 0.00000 5084.3023
## 90 0.00000 5084.3023
## 91 0.00000 5084.3023
## 92 0.00000 5084.3023
## 93 0.00000 5084.3023
## 94 0.00000 5084.3023
## 95 0.00000 5084.3023
## 96 0.00000 5084.3023
## 97 0.00000 5241.2791
## 98 0.00000 5241.2791
## 99 0.00000 5241.2791
## 100 0.00000 5241.2791
## 101 0.00000 5241.2791
## 102 0.00000 5241.2791
## 103 0.00000 5241.2791
## 104 0.00000 5241.2791
## 105 0.00000 5241.2791
## 106 0.00000 5241.2791
## 107 0.00000 5241.2791
## 108 0.00000 5241.2791
## 109 0.00000 5241.2791
## 110 0.00000 5241.2791
## 111 0.00000 5241.2791
## 112 0.00000 5241.2791
## 113 0.00000 5241.2791
## 114 0.00000 5241.2791
## 115 0.00000 5241.2791
## 116 0.00000 5241.2791
## 117 0.00000 5241.2791
## 118 0.00000 5241.2791
## 119 0.00000 5241.2791
## 120 0.00000 5241.2791
## 121 0.00000 2956.3953
## 122 0.00000 2956.3953
## 123 0.00000 2956.3953
## 124 0.00000 2956.3953
## 125 0.00000 2956.3953
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## 135 0.00000 2956.3953
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## 139 0.00000 2956.3953
## 140 0.00000 2956.3953
## 141 0.00000 2956.3953
## 142 0.00000 2956.3953
## 143 0.00000 2956.3953
## 144 0.00000 2956.3953
## 145 0.00000 662.7907
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## 149 0.00000 662.7907
## 150 0.00000 662.7907
## 151 0.00000 662.7907
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## 153 0.00000 662.7907
## 154 0.00000 662.7907
## 155 0.00000 662.7907
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## 158 0.00000 662.7907
## 159 0.00000 662.7907
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## 161 0.00000 662.7907
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## 163 0.00000 662.7907
## 164 0.00000 662.7907
## 165 0.00000 662.7907
## 166 0.00000 662.7907
## 167 0.00000 662.7907
## 168 0.00000 662.7907
## 169 1090.11628 2428.7791
## 170 1090.11628 2428.7791
## 171 1090.11628 2428.7791
## 172 1090.11628 2428.7791
## 173 1090.11628 2428.7791
## 174 1090.11628 2428.7791
## 175 1090.11628 2428.7791
## 176 1090.11628 2428.7791
## 177 1090.11628 2428.7791
## 178 1090.11628 2428.7791
## 179 1090.11628 2428.7791
## 180 1090.11628 2428.7791
## 181 1090.11628 2428.7791
## 182 1090.11628 2428.7791
## 183 1090.11628 2428.7791
## 184 1090.11628 2428.7791
## 185 1090.11628 2428.7791
## 186 1090.11628 2428.7791
## 187 1090.11628 2428.7791
## 188 1090.11628 2428.7791
## 189 1090.11628 2428.7791
## 190 1090.11628 2428.7791
## 191 1090.11628 2428.7791
## 192 1090.11628 2428.7791
## 193 7834.88372 3155.2326
## 194 7834.88372 3155.2326
## 195 7834.88372 3155.2326
## 196 7834.88372 3155.2326
## 197 7834.88372 3155.2326
## 198 7834.88372 3155.2326
## 199 7834.88372 3155.2326
## 200 7834.88372 3155.2326
## 201 7834.88372 3155.2326
## 202 7834.88372 3155.2326
## 203 7834.88372 3155.2326
## 204 7834.88372 3155.2326
## 205 7834.88372 3155.2326
## 206 7834.88372 3155.2326
## 207 7834.88372 3155.2326
## 208 7834.88372 3155.2326
## 209 7834.88372 3155.2326
## 210 7834.88372 3155.2326
## 211 7834.88372 3155.2326
## 212 7834.88372 3155.2326
## 213 7834.88372 3155.2326
## 214 7834.88372 3155.2326
## 215 7834.88372 3155.2326
## 216 7834.88372 3155.2326
## 217 1941.27907 8312.7907
## 218 1941.27907 8312.7907
## 219 1941.27907 8312.7907
## 220 1941.27907 8312.7907
## 221 1941.27907 8312.7907
## 222 1941.27907 8312.7907
## 223 1941.27907 8312.7907
## 224 1941.27907 8312.7907
## 225 1941.27907 8312.7907
## 226 1941.27907 8312.7907
## 227 1941.27907 8312.7907
## 228 1941.27907 8312.7907
## 229 1941.27907 8312.7907
## 230 1941.27907 8312.7907
## 231 1941.27907 8312.7907
## 232 1941.27907 8312.7907
## 233 1941.27907 8312.7907
## 234 1941.27907 8312.7907
## 235 1941.27907 8312.7907
## 236 1941.27907 8312.7907
## 237 1941.27907 8312.7907
## 238 1941.27907 8312.7907
## 239 1941.27907 8312.7907
## 240 1941.27907 8312.7907
## 241 218.02326 3071.5116
## 242 218.02326 3071.5116
## 243 218.02326 3071.5116
## 244 218.02326 3071.5116
## 245 218.02326 3071.5116
## 246 218.02326 3071.5116
## 247 218.02326 3071.5116
## 248 218.02326 3071.5116
## 249 218.02326 3071.5116
## 250 218.02326 3071.5116
## 251 218.02326 3071.5116
## 252 218.02326 3071.5116
## 253 218.02326 3071.5116
## 254 218.02326 3071.5116
## 255 218.02326 3071.5116
## 256 218.02326 3071.5116
## 257 218.02326 3071.5116
## 258 218.02326 3071.5116
## 259 218.02326 3071.5116
## 260 218.02326 3071.5116
## 261 218.02326 3071.5116
## 262 218.02326 3071.5116
## 263 218.02326 3071.5116
## 264 218.02326 3071.5116
## 265 1438.95349 4988.3721
## 266 1438.95349 4988.3721
## 267 1438.95349 4988.3721
## 268 1438.95349 4988.3721
## 269 1438.95349 4988.3721
## 270 1438.95349 4988.3721
## 271 1438.95349 4988.3721
## 272 1438.95349 4988.3721
## 273 1438.95349 4988.3721
## 274 1438.95349 4988.3721
## 275 1438.95349 4988.3721
## 276 1438.95349 4988.3721
## 277 1438.95349 4988.3721
## 278 1438.95349 4988.3721
## 279 1438.95349 4988.3721
## 280 1438.95349 4988.3721
## 281 1438.95349 4988.3721
## 282 1438.95349 4988.3721
## 283 1438.95349 4988.3721
## 284 1438.95349 4988.3721
## 285 1438.95349 4988.3721
## 286 1438.95349 4988.3721
## 287 1438.95349 4988.3721
## 288 1438.95349 4988.3721
## 289 0.00000 4534.8837
## 290 0.00000 4534.8837
## 291 0.00000 4534.8837
## 292 0.00000 4534.8837
## 293 0.00000 4534.8837
## 294 0.00000 4534.8837
## 295 0.00000 4534.8837
## 296 0.00000 4534.8837
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## 298 0.00000 4534.8837
## 299 0.00000 4534.8837
## 300 0.00000 4534.8837
## 301 0.00000 4534.8837
## 302 0.00000 4534.8837
## 303 0.00000 4534.8837
## 304 0.00000 4534.8837
## 305 0.00000 4534.8837
## 306 0.00000 4534.8837
## 307 0.00000 4534.8837
## 308 0.00000 4534.8837
## 309 0.00000 4534.8837
## 310 0.00000 4534.8837
## 311 0.00000 4534.8837
## 312 0.00000 4534.8837
## 313 0.00000 1456.3953
## 314 0.00000 1456.3953
## 315 0.00000 1456.3953
## 316 0.00000 1456.3953
## 317 0.00000 1456.3953
## 318 0.00000 1456.3953
## 319 0.00000 1456.3953
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## 325 0.00000 1456.3953
## 326 0.00000 1456.3953
## 327 0.00000 1456.3953
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## 332 0.00000 1456.3953
## 333 0.00000 1456.3953
## 334 0.00000 1456.3953
## 335 0.00000 1456.3953
## 336 0.00000 1456.3953
## 337 1312.50000 1059.5930
## 338 1312.50000 1059.5930
## 339 1312.50000 1059.5930
## 340 1312.50000 1059.5930
## 341 1312.50000 1059.5930
## 342 1312.50000 1059.5930
## 343 1312.50000 1059.5930
## 344 1312.50000 1059.5930
## 345 1312.50000 1059.5930
## 346 1312.50000 1059.5930
## 347 1312.50000 1059.5930
## 348 1312.50000 1059.5930
## 349 1312.50000 1059.5930
## 350 1312.50000 1059.5930
## 351 1312.50000 1059.5930
## 352 1312.50000 1059.5930
## 353 1312.50000 1059.5930
## 354 1312.50000 1059.5930
## 355 1312.50000 1059.5930
## 356 1312.50000 1059.5930
## 357 1312.50000 1059.5930
## 358 1312.50000 1059.5930
## 359 1312.50000 1059.5930
## 360 1312.50000 1059.5930
## 361 1962.20930 1360.4651
## 362 1962.20930 1360.4651
## 363 1962.20930 1360.4651
## 364 1962.20930 1360.4651
## 365 1962.20930 1360.4651
## 366 1962.20930 1360.4651
## 367 1962.20930 1360.4651
## 368 1962.20930 1360.4651
## 369 1962.20930 1360.4651
## 370 1962.20930 1360.4651
## 371 1962.20930 1360.4651
## 372 1962.20930 1360.4651
## 373 1962.20930 1360.4651
## 374 1962.20930 1360.4651
## 375 1962.20930 1360.4651
## 376 1962.20930 1360.4651
## 377 1962.20930 1360.4651
## 378 1962.20930 1360.4651
## 379 1962.20930 1360.4651
## 380 1962.20930 1360.4651
## 381 1962.20930 1360.4651
## 382 1962.20930 1360.4651
## 383 1962.20930 1360.4651
## 384 1962.20930 1360.4651
## 385 7834.88372 3155.2326
## 386 7834.88372 3155.2326
## 387 7834.88372 3155.2326
## 388 7834.88372 3155.2326
## 389 7834.88372 3155.2326
## 390 7834.88372 3155.2326
## 391 7834.88372 3155.2326
## 392 7834.88372 3155.2326
## 393 7834.88372 3155.2326
## 394 7834.88372 3155.2326
## 395 7834.88372 3155.2326
## 396 7834.88372 3155.2326
## 397 7834.88372 3155.2326
## 398 7834.88372 3155.2326
## 399 7834.88372 3155.2326
## 400 7834.88372 3155.2326
## 401 7834.88372 3155.2326
## 402 7834.88372 3155.2326
## 403 7834.88372 3155.2326
## 404 7834.88372 3155.2326
## 405 7834.88372 3155.2326
## 406 7834.88372 3155.2326
## 407 7834.88372 3155.2326
## 408 7834.88372 3155.2326
## 409 2263.95349 2190.6977
## 410 2263.95349 2190.6977
## 411 2263.95349 2190.6977
## 412 2263.95349 2190.6977
## 413 2263.95349 2190.6977
## 414 2263.95349 2190.6977
## 415 2263.95349 2190.6977
## 416 2263.95349 2190.6977
## 417 2263.95349 2190.6977
## 418 2263.95349 2190.6977
## 419 2263.95349 2190.6977
## 420 2263.95349 2190.6977
## 421 2263.95349 2190.6977
## 422 2263.95349 2190.6977
## 423 2263.95349 2190.6977
## 424 2263.95349 2190.6977
## 425 2263.95349 2190.6977
## 426 2263.95349 2190.6977
## 427 2263.95349 2190.6977
## 428 2263.95349 2190.6977
## 429 2263.95349 2190.6977
## 430 2263.95349 2190.6977
## 431 2263.95349 2190.6977
## 432 2263.95349 2190.6977
## 433 218.02326 6586.0465
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## 435 218.02326 6586.0465
## 436 218.02326 6586.0465
## 437 218.02326 6586.0465
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## 439 218.02326 6586.0465
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## 441 218.02326 6586.0465
## 442 218.02326 6586.0465
## 443 218.02326 6586.0465
## 444 218.02326 6586.0465
## 445 218.02326 6586.0465
## 446 218.02326 6586.0465
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## 449 218.02326 6586.0465
## 450 218.02326 6586.0465
## 451 218.02326 6586.0465
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## 453 218.02326 6586.0465
## 454 218.02326 6586.0465
## 455 218.02326 6586.0465
## 456 218.02326 6586.0465
## 457 837.20930 3453.4884
## 458 837.20930 3453.4884
## 459 837.20930 3453.4884
## 460 837.20930 3453.4884
## 461 837.20930 3453.4884
## 462 837.20930 3453.4884
## 463 837.20930 3453.4884
## 464 837.20930 3453.4884
## 465 837.20930 3453.4884
## 466 837.20930 3453.4884
## 467 837.20930 3453.4884
## 468 837.20930 3453.4884
## 469 837.20930 3453.4884
## 470 837.20930 3453.4884
## 471 837.20930 3453.4884
## 472 837.20930 3453.4884
## 473 837.20930 3453.4884
## 474 837.20930 3453.4884
## 475 837.20930 3453.4884
## 476 837.20930 3453.4884
## 477 837.20930 3453.4884
## 478 837.20930 3453.4884
## 479 837.20930 3453.4884
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## 2841 1609.01163 1046.5116
## 2842 1609.01163 1046.5116
## 2843 1609.01163 1046.5116
## 2844 1609.01163 1046.5116
## 2845 1609.01163 1046.5116
## 2846 1609.01163 1046.5116
## 2847 1609.01163 1046.5116
## 2848 1609.01163 1046.5116
## 2849 1609.01163 1046.5116
## 2850 1609.01163 1046.5116
## 2851 1609.01163 1046.5116
## 2852 1609.01163 1046.5116
## 2853 1609.01163 1046.5116
## 2854 1609.01163 1046.5116
## 2855 1609.01163 1046.5116
## 2856 1609.01163 1046.5116
## 2857 837.20930 1090.1163
## 2858 837.20930 1090.1163
## 2859 837.20930 1090.1163
## 2860 837.20930 1090.1163
## 2861 837.20930 1090.1163
## 2862 837.20930 1090.1163
## 2863 837.20930 1090.1163
## 2864 837.20930 1090.1163
## 2865 837.20930 1090.1163
## 2866 837.20930 1090.1163
## 2867 837.20930 1090.1163
## 2868 837.20930 1090.1163
## 2869 837.20930 1090.1163
## 2870 837.20930 1090.1163
## 2871 837.20930 1090.1163
## 2872 837.20930 1090.1163
## 2873 837.20930 1090.1163
## 2874 837.20930 1090.1163
## 2875 837.20930 1090.1163
## 2876 837.20930 1090.1163
## 2877 837.20930 1090.1163
## 2878 837.20930 1090.1163
## 2879 837.20930 1090.1163
## 2880 837.20930 1090.1163
## 2881 7834.88372 3155.2326
## 2882 7834.88372 3155.2326
## 2883 7834.88372 3155.2326
## 2884 7834.88372 3155.2326
## 2885 7834.88372 3155.2326
## 2886 7834.88372 3155.2326
## 2887 7834.88372 3155.2326
## 2888 7834.88372 3155.2326
## 2889 7834.88372 3155.2326
## 2890 7834.88372 3155.2326
## 2891 7834.88372 3155.2326
## 2892 7834.88372 3155.2326
## 2893 7834.88372 3155.2326
## 2894 7834.88372 3155.2326
## 2895 7834.88372 3155.2326
## 2896 7834.88372 3155.2326
## 2897 7834.88372 3155.2326
## 2898 7834.88372 3155.2326
## 2899 7834.88372 3155.2326
## 2900 7834.88372 3155.2326
## 2901 7834.88372 3155.2326
## 2902 7834.88372 3155.2326
## 2903 7834.88372 3155.2326
## 2904 7834.88372 3155.2326
## 2905 2398.25581 3811.0465
## 2906 2398.25581 3811.0465
## 2907 2398.25581 3811.0465
## 2908 2398.25581 3811.0465
## 2909 2398.25581 3811.0465
## 2910 2398.25581 3811.0465
## 2911 2398.25581 3811.0465
## 2912 2398.25581 3811.0465
## 2913 2398.25581 3811.0465
## 2914 2398.25581 3811.0465
## 2915 2398.25581 3811.0465
## 2916 2398.25581 3811.0465
## 2917 2398.25581 3811.0465
## 2918 2398.25581 3811.0465
## 2919 2398.25581 3811.0465
## 2920 2398.25581 3811.0465
## 2921 2398.25581 3811.0465
## 2922 2398.25581 3811.0465
## 2923 2398.25581 3811.0465
## 2924 2398.25581 3811.0465
## 2925 2398.25581 3811.0465
## 2926 2398.25581 3811.0465
## 2927 2398.25581 3811.0465
## 2928 2398.25581 3811.0465
## 2929 127.32558 1887.2093
## 2930 127.32558 1887.2093
## 2931 127.32558 1887.2093
## 2932 127.32558 1887.2093
## 2933 127.32558 1887.2093
## 2934 127.32558 1887.2093
## 2935 127.32558 1887.2093
## 2936 127.32558 1887.2093
## 2937 127.32558 1887.2093
## 2938 127.32558 1887.2093
## 2939 127.32558 1887.2093
## 2940 127.32558 1887.2093
## 2941 127.32558 1887.2093
## 2942 127.32558 1887.2093
## 2943 127.32558 1887.2093
## 2944 127.32558 1887.2093
## 2945 127.32558 1887.2093
## 2946 127.32558 1887.2093
## 2947 127.32558 1887.2093
## 2948 127.32558 1887.2093
## 2949 127.32558 1887.2093
## 2950 127.32558 1887.2093
## 2951 127.32558 1887.2093
## 2952 127.32558 1887.2093
## 2953 0.00000 5084.3023
## 2954 0.00000 5084.3023
## 2955 0.00000 5084.3023
## 2956 0.00000 5084.3023
## 2957 0.00000 5084.3023
## 2958 0.00000 5084.3023
## 2959 0.00000 5084.3023
## 2960 0.00000 5084.3023
## 2961 0.00000 5084.3023
## 2962 0.00000 5084.3023
## 2963 0.00000 5084.3023
## 2964 0.00000 5084.3023
## 2965 0.00000 5084.3023
## 2966 0.00000 5084.3023
## 2967 0.00000 5084.3023
## 2968 0.00000 5084.3023
## 2969 0.00000 5084.3023
## 2970 0.00000 5084.3023
## 2971 0.00000 5084.3023
## 2972 0.00000 5084.3023
## 2973 0.00000 5084.3023
## 2974 0.00000 5084.3023
## 2975 0.00000 5084.3023
## 2976 0.00000 5084.3023
## 2977 0.00000 4822.6744
## 2978 0.00000 4822.6744
## 2979 0.00000 4822.6744
## 2980 0.00000 4822.6744
## 2981 0.00000 4822.6744
## 2982 0.00000 4822.6744
## 2983 0.00000 4822.6744
## 2984 0.00000 4822.6744
## 2985 0.00000 4822.6744
## 2986 0.00000 4822.6744
## 2987 0.00000 4822.6744
## 2988 0.00000 4822.6744
## 2989 0.00000 4822.6744
## 2990 0.00000 4822.6744
## 2991 0.00000 4822.6744
## 2992 0.00000 4822.6744
## 2993 0.00000 4822.6744
## 2994 0.00000 4822.6744
## 2995 0.00000 4822.6744
## 2996 0.00000 4822.6744
## 2997 0.00000 4822.6744
## 2998 0.00000 4822.6744
## 2999 0.00000 4822.6744
## 3000 0.00000 4822.6744
## 3001 0.00000 3174.4186
## 3002 0.00000 3174.4186
## 3003 0.00000 3174.4186
## 3004 0.00000 3174.4186
## 3005 0.00000 3174.4186
## 3006 0.00000 3174.4186
## 3007 0.00000 3174.4186
## 3008 0.00000 3174.4186
## 3009 0.00000 3174.4186
## 3010 0.00000 3174.4186
## 3011 0.00000 3174.4186
## 3012 0.00000 3174.4186
## 3013 0.00000 3174.4186
## 3014 0.00000 3174.4186
## 3015 0.00000 3174.4186
## 3016 0.00000 3174.4186
## 3017 0.00000 3174.4186
## 3018 0.00000 3174.4186
## 3019 0.00000 3174.4186
## 3020 0.00000 3174.4186
## 3021 0.00000 3174.4186
## 3022 0.00000 3174.4186
## 3023 0.00000 3174.4186
## 3024 0.00000 3174.4186
## 3025 0.00000 662.7907
## 3026 0.00000 662.7907
## 3027 0.00000 662.7907
## 3028 0.00000 662.7907
## 3029 0.00000 662.7907
## 3030 0.00000 662.7907
## 3031 0.00000 662.7907
## 3032 0.00000 662.7907
## 3033 0.00000 662.7907
## 3034 0.00000 662.7907
## 3035 0.00000 662.7907
## 3036 0.00000 662.7907
## 3037 0.00000 662.7907
## 3038 0.00000 662.7907
## 3039 0.00000 662.7907
## 3040 0.00000 662.7907
## 3041 0.00000 662.7907
## 3042 0.00000 662.7907
## 3043 0.00000 662.7907
## 3044 0.00000 662.7907
## 3045 0.00000 662.7907
## 3046 0.00000 662.7907
## 3047 0.00000 662.7907
## 3048 0.00000 662.7907
## 3049 1090.11628 2646.8023
## 3050 1090.11628 2646.8023
## 3051 1090.11628 2646.8023
## 3052 1090.11628 2646.8023
## 3053 1090.11628 2646.8023
## 3054 1090.11628 2646.8023
## 3055 1090.11628 2646.8023
## 3056 1090.11628 2646.8023
## 3057 1090.11628 2646.8023
## 3058 1090.11628 2646.8023
## 3059 1090.11628 2646.8023
## 3060 1090.11628 2646.8023
## 3061 1090.11628 2646.8023
## 3062 1090.11628 2646.8023
## 3063 1090.11628 2646.8023
## 3064 1090.11628 2646.8023
## 3065 1090.11628 2646.8023
## 3066 1090.11628 2646.8023
## 3067 1090.11628 2646.8023
## 3068 1090.11628 2646.8023
## 3069 1090.11628 2646.8023
## 3070 1090.11628 2646.8023
## 3071 1090.11628 2646.8023
## 3072 1090.11628 2646.8023
## 3073 7834.88372 3155.2326
## 3074 7834.88372 3155.2326
## 3075 7834.88372 3155.2326
## 3076 7834.88372 3155.2326
## 3077 7834.88372 3155.2326
## 3078 7834.88372 3155.2326
## 3079 7834.88372 3155.2326
## 3080 7834.88372 3155.2326
## 3081 7834.88372 3155.2326
## 3082 7834.88372 3155.2326
## 3083 7834.88372 3155.2326
## 3084 7834.88372 3155.2326
## 3085 7834.88372 3155.2326
## 3086 7834.88372 3155.2326
## 3087 7834.88372 3155.2326
## 3088 7834.88372 3155.2326
## 3089 7834.88372 3155.2326
## 3090 7834.88372 3155.2326
## 3091 7834.88372 3155.2326
## 3092 7834.88372 3155.2326
## 3093 7834.88372 3155.2326
## 3094 7834.88372 3155.2326
## 3095 7834.88372 3155.2326
## 3096 7834.88372 3155.2326
## 3097 1281.97674 7639.5349
## 3098 1281.97674 7639.5349
## 3099 1281.97674 7639.5349
## 3100 1281.97674 7639.5349
## 3101 1281.97674 7639.5349
## 3102 1281.97674 7639.5349
## 3103 1281.97674 7639.5349
## 3104 1281.97674 7639.5349
## 3105 1281.97674 7639.5349
## 3106 1281.97674 7639.5349
## 3107 1281.97674 7639.5349
## 3108 1281.97674 7639.5349
## 3109 1281.97674 7639.5349
## 3110 1281.97674 7639.5349
## 3111 1281.97674 7639.5349
## 3112 1281.97674 7639.5349
## 3113 1281.97674 7639.5349
## 3114 1281.97674 7639.5349
## 3115 1281.97674 7639.5349
## 3116 1281.97674 7639.5349
## 3117 1281.97674 7639.5349
## 3118 1281.97674 7639.5349
## 3119 1281.97674 7639.5349
## 3120 1281.97674 7639.5349
## 3121 127.32558 3640.1163
## 3122 127.32558 3640.1163
## 3123 127.32558 3640.1163
## 3124 127.32558 3640.1163
## 3125 127.32558 3640.1163
## 3126 127.32558 3640.1163
## 3127 127.32558 3640.1163
## 3128 127.32558 3640.1163
## 3129 127.32558 3640.1163
## 3130 127.32558 3640.1163
## 3131 127.32558 3640.1163
## 3132 127.32558 3640.1163
## 3133 127.32558 3640.1163
## 3134 127.32558 3640.1163
## 3135 127.32558 3640.1163
## 3136 127.32558 3640.1163
## 3137 127.32558 3640.1163
## 3138 127.32558 3640.1163
## 3139 127.32558 3640.1163
## 3140 127.32558 3640.1163
## 3141 127.32558 3640.1163
## 3142 127.32558 3640.1163
## 3143 127.32558 3640.1163
## 3144 127.32558 3640.1163
## 3145 1438.95349 4988.3721
## 3146 1438.95349 4988.3721
## 3147 1438.95349 4988.3721
## 3148 1438.95349 4988.3721
## 3149 1438.95349 4988.3721
## 3150 1438.95349 4988.3721
## 3151 1438.95349 4988.3721
## 3152 1438.95349 4988.3721
## 3153 1438.95349 4988.3721
## 3154 1438.95349 4988.3721
## 3155 1438.95349 4988.3721
## 3156 1438.95349 4988.3721
## 3157 1438.95349 4988.3721
## 3158 1438.95349 4988.3721
## 3159 1438.95349 4988.3721
## 3160 1438.95349 4988.3721
## 3161 1438.95349 4988.3721
## 3162 1438.95349 4988.3721
## 3163 1438.95349 4988.3721
## 3164 1438.95349 4988.3721
## 3165 1438.95349 4988.3721
## 3166 1438.95349 4988.3721
## 3167 1438.95349 4988.3721
## 3168 1438.95349 4988.3721
## 3169 0.00000 4116.2791
## 3170 0.00000 4116.2791
## 3171 0.00000 4116.2791
## 3172 0.00000 4116.2791
## 3173 0.00000 4116.2791
## 3174 0.00000 4116.2791
## 3175 0.00000 4116.2791
## 3176 0.00000 4116.2791
## 3177 0.00000 4116.2791
## 3178 0.00000 4116.2791
## 3179 0.00000 4116.2791
## 3180 0.00000 4116.2791
## 3181 0.00000 4116.2791
## 3182 0.00000 4116.2791
## 3183 0.00000 4116.2791
## 3184 0.00000 4116.2791
## 3185 0.00000 4116.2791
## 3186 0.00000 4116.2791
## 3187 0.00000 4116.2791
## 3188 0.00000 4116.2791
## 3189 0.00000 4116.2791
## 3190 0.00000 4116.2791
## 3191 0.00000 4116.2791
## 3192 0.00000 4116.2791
## 3193 0.00000 1674.4186
## 3194 0.00000 1674.4186
## 3195 0.00000 1674.4186
## 3196 0.00000 1674.4186
## 3197 0.00000 1674.4186
## 3198 0.00000 1674.4186
## 3199 0.00000 1674.4186
## 3200 0.00000 1674.4186
## 3201 0.00000 1674.4186
## 3202 0.00000 1674.4186
## 3203 0.00000 1674.4186
## 3204 0.00000 1674.4186
## 3205 0.00000 1674.4186
## 3206 0.00000 1674.4186
## 3207 0.00000 1674.4186
## 3208 0.00000 1674.4186
## 3209 0.00000 1674.4186
## 3210 0.00000 1674.4186
## 3211 0.00000 1674.4186
## 3212 0.00000 1674.4186
## 3213 0.00000 1674.4186
## 3214 0.00000 1674.4186
## 3215 0.00000 1674.4186
## 3216 0.00000 1674.4186
## 3217 1312.50000 1059.5930
## 3218 1312.50000 1059.5930
## 3219 1312.50000 1059.5930
## 3220 1312.50000 1059.5930
## 3221 1312.50000 1059.5930
## 3222 1312.50000 1059.5930
## 3223 1312.50000 1059.5930
## 3224 1312.50000 1059.5930
## 3225 1312.50000 1059.5930
## total_metaecosystem_Lox_indiv total_metaecosystem_Pau_indiv
## 1 2494.1860 5161.0465
## 2 2494.1860 5161.0465
## 3 2494.1860 5161.0465
## 4 2494.1860 5161.0465
## 5 2494.1860 5161.0465
## 6 2494.1860 5161.0465
## 7 2494.1860 5161.0465
## 8 2494.1860 5161.0465
## 9 2494.1860 5161.0465
## 10 2494.1860 5161.0465
## 11 2494.1860 5161.0465
## 12 2494.1860 5161.0465
## 13 2494.1860 5161.0465
## 14 2494.1860 5161.0465
## 15 2494.1860 5161.0465
## 16 2494.1860 5161.0465
## 17 2494.1860 5161.0465
## 18 2494.1860 5161.0465
## 19 2494.1860 5161.0465
## 20 2494.1860 5161.0465
## 21 2494.1860 5161.0465
## 22 2494.1860 5161.0465
## 23 2494.1860 5161.0465
## 24 2494.1860 5161.0465
## 25 218.0233 0.0000
## 26 218.0233 0.0000
## 27 218.0233 0.0000
## 28 218.0233 0.0000
## 29 218.0233 0.0000
## 30 218.0233 0.0000
## 31 218.0233 0.0000
## 32 218.0233 0.0000
## 33 218.0233 0.0000
## 34 218.0233 0.0000
## 35 218.0233 0.0000
## 36 218.0233 0.0000
## 37 218.0233 0.0000
## 38 218.0233 0.0000
## 39 218.0233 0.0000
## 40 218.0233 0.0000
## 41 218.0233 0.0000
## 42 218.0233 0.0000
## 43 218.0233 0.0000
## 44 218.0233 0.0000
## 45 218.0233 0.0000
## 46 218.0233 0.0000
## 47 218.0233 0.0000
## 48 218.0233 0.0000
## 49 0.0000 1970.9302
## 50 0.0000 1970.9302
## 51 0.0000 1970.9302
## 52 0.0000 1970.9302
## 53 0.0000 1970.9302
## 54 0.0000 1970.9302
## 55 0.0000 1970.9302
## 56 0.0000 1970.9302
## 57 0.0000 1970.9302
## 58 0.0000 1970.9302
## 59 0.0000 1970.9302
## 60 0.0000 1970.9302
## 61 0.0000 1970.9302
## 62 0.0000 1970.9302
## 63 0.0000 1970.9302
## 64 0.0000 1970.9302
## 65 0.0000 1970.9302
## 66 0.0000 1970.9302
## 67 0.0000 1970.9302
## 68 0.0000 1970.9302
## 69 0.0000 1970.9302
## 70 0.0000 1970.9302
## 71 0.0000 1970.9302
## 72 0.0000 1970.9302
## 73 0.0000 1369.1860
## 74 0.0000 1369.1860
## 75 0.0000 1369.1860
## 76 0.0000 1369.1860
## 77 0.0000 1369.1860
## 78 0.0000 1369.1860
## 79 0.0000 1369.1860
## 80 0.0000 1369.1860
## 81 0.0000 1369.1860
## 82 0.0000 1369.1860
## 83 0.0000 1369.1860
## 84 0.0000 1369.1860
## 85 0.0000 1369.1860
## 86 0.0000 1369.1860
## 87 0.0000 1369.1860
## 88 0.0000 1369.1860
## 89 0.0000 1369.1860
## 90 0.0000 1369.1860
## 91 0.0000 1369.1860
## 92 0.0000 1369.1860
## 93 0.0000 1369.1860
## 94 0.0000 1369.1860
## 95 0.0000 1369.1860
## 96 0.0000 1369.1860
## 97 436.0465 2776.7442
## 98 436.0465 2776.7442
## 99 436.0465 2776.7442
## 100 436.0465 2776.7442
## 101 436.0465 2776.7442
## 102 436.0465 2776.7442
## 103 436.0465 2776.7442
## 104 436.0465 2776.7442
## 105 436.0465 2776.7442
## 106 436.0465 2776.7442
## 107 436.0465 2776.7442
## 108 436.0465 2776.7442
## 109 436.0465 2776.7442
## 110 436.0465 2776.7442
## 111 436.0465 2776.7442
## 112 436.0465 2776.7442
## 113 436.0465 2776.7442
## 114 436.0465 2776.7442
## 115 436.0465 2776.7442
## 116 436.0465 2776.7442
## 117 436.0465 2776.7442
## 118 436.0465 2776.7442
## 119 436.0465 2776.7442
## 120 436.0465 2776.7442
## 121 0.0000 559.8837
## 122 0.0000 559.8837
## 123 0.0000 559.8837
## 124 0.0000 559.8837
## 125 0.0000 559.8837
## 126 0.0000 559.8837
## 127 0.0000 559.8837
## 128 0.0000 559.8837
## 129 0.0000 559.8837
## 130 0.0000 559.8837
## 131 0.0000 559.8837
## 132 0.0000 559.8837
## 133 0.0000 559.8837
## 134 0.0000 559.8837
## 135 0.0000 559.8837
## 136 0.0000 559.8837
## 137 0.0000 559.8837
## 138 0.0000 559.8837
## 139 0.0000 559.8837
## 140 0.0000 559.8837
## 141 0.0000 559.8837
## 142 0.0000 559.8837
## 143 0.0000 559.8837
## 144 0.0000 559.8837
## 145 545.0581 109.0116
## 146 545.0581 109.0116
## 147 545.0581 109.0116
## 148 545.0581 109.0116
## 149 545.0581 109.0116
## 150 545.0581 109.0116
## 151 545.0581 109.0116
## 152 545.0581 109.0116
## 153 545.0581 109.0116
## 154 545.0581 109.0116
## 155 545.0581 109.0116
## 156 545.0581 109.0116
## 157 545.0581 109.0116
## 158 545.0581 109.0116
## 159 545.0581 109.0116
## 160 545.0581 109.0116
## 161 545.0581 109.0116
## 162 545.0581 109.0116
## 163 545.0581 109.0116
## 164 545.0581 109.0116
## 165 545.0581 109.0116
## 166 545.0581 109.0116
## 167 545.0581 109.0116
## 168 545.0581 109.0116
## 169 1630.8140 531.9767
## 170 1630.8140 531.9767
## 171 1630.8140 531.9767
## 172 1630.8140 531.9767
## 173 1630.8140 531.9767
## 174 1630.8140 531.9767
## 175 1630.8140 531.9767
## 176 1630.8140 531.9767
## 177 1630.8140 531.9767
## 178 1630.8140 531.9767
## 179 1630.8140 531.9767
## 180 1630.8140 531.9767
## 181 1630.8140 531.9767
## 182 1630.8140 531.9767
## 183 1630.8140 531.9767
## 184 1630.8140 531.9767
## 185 1630.8140 531.9767
## 186 1630.8140 531.9767
## 187 1630.8140 531.9767
## 188 1630.8140 531.9767
## 189 1630.8140 531.9767
## 190 1630.8140 531.9767
## 191 1630.8140 531.9767
## 192 1630.8140 531.9767
## 193 2494.1860 5161.0465
## 194 2494.1860 5161.0465
## 195 2494.1860 5161.0465
## 196 2494.1860 5161.0465
## 197 2494.1860 5161.0465
## 198 2494.1860 5161.0465
## 199 2494.1860 5161.0465
## 200 2494.1860 5161.0465
## 201 2494.1860 5161.0465
## 202 2494.1860 5161.0465
## 203 2494.1860 5161.0465
## 204 2494.1860 5161.0465
## 205 2494.1860 5161.0465
## 206 2494.1860 5161.0465
## 207 2494.1860 5161.0465
## 208 2494.1860 5161.0465
## 209 2494.1860 5161.0465
## 210 2494.1860 5161.0465
## 211 2494.1860 5161.0465
## 212 2494.1860 5161.0465
## 213 2494.1860 5161.0465
## 214 2494.1860 5161.0465
## 215 2494.1860 5161.0465
## 216 2494.1860 5161.0465
## 217 1438.9535 5720.9302
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## 1980 0.0000 1854.0698
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## 1986 0.0000 1854.0698
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## 1989 0.0000 1854.0698
## 1990 0.0000 1854.0698
## 1991 0.0000 1854.0698
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## 1994 0.0000 1852.3256
## 1995 0.0000 1852.3256
## 1996 0.0000 1852.3256
## 1997 0.0000 1852.3256
## 1998 0.0000 1852.3256
## 1999 0.0000 1852.3256
## 2000 0.0000 1852.3256
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## 2003 0.0000 1852.3256
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## 2005 0.0000 1852.3256
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## 2007 0.0000 1852.3256
## 2008 0.0000 1852.3256
## 2009 0.0000 1852.3256
## 2010 0.0000 1852.3256
## 2011 0.0000 1852.3256
## 2012 0.0000 1852.3256
## 2013 0.0000 1852.3256
## 2014 0.0000 1852.3256
## 2015 0.0000 1852.3256
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## 2017 0.0000 2572.6744
## 2018 0.0000 2572.6744
## 2019 0.0000 2572.6744
## 2020 0.0000 2572.6744
## 2021 0.0000 2572.6744
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## 2702 2494.1860 5161.0465
## 2703 2494.1860 5161.0465
## 2704 2494.1860 5161.0465
## 2705 2494.1860 5161.0465
## 2706 2494.1860 5161.0465
## 2707 2494.1860 5161.0465
## 2708 2494.1860 5161.0465
## 2709 2494.1860 5161.0465
## 2710 2494.1860 5161.0465
## 2711 2494.1860 5161.0465
## 2712 2494.1860 5161.0465
## 2713 2154.0698 4037.7907
## 2714 2154.0698 4037.7907
## 2715 2154.0698 4037.7907
## 2716 2154.0698 4037.7907
## 2717 2154.0698 4037.7907
## 2718 2154.0698 4037.7907
## 2719 2154.0698 4037.7907
## 2720 2154.0698 4037.7907
## 2721 2154.0698 4037.7907
## 2722 2154.0698 4037.7907
## 2723 2154.0698 4037.7907
## 2724 2154.0698 4037.7907
## 2725 2154.0698 4037.7907
## 2726 2154.0698 4037.7907
## 2727 2154.0698 4037.7907
## 2728 2154.0698 4037.7907
## 2729 2154.0698 4037.7907
## 2730 2154.0698 4037.7907
## 2731 2154.0698 4037.7907
## 2732 2154.0698 4037.7907
## 2733 2154.0698 4037.7907
## 2734 2154.0698 4037.7907
## 2735 2154.0698 4037.7907
## 2736 2154.0698 4037.7907
## 2737 0.0000 450.0000
## 2738 0.0000 450.0000
## 2739 0.0000 450.0000
## 2740 0.0000 450.0000
## 2741 0.0000 450.0000
## 2742 0.0000 450.0000
## 2743 0.0000 450.0000
## 2744 0.0000 450.0000
## 2745 0.0000 450.0000
## 2746 0.0000 450.0000
## 2747 0.0000 450.0000
## 2748 0.0000 450.0000
## 2749 0.0000 450.0000
## 2750 0.0000 450.0000
## 2751 0.0000 450.0000
## 2752 0.0000 450.0000
## 2753 0.0000 450.0000
## 2754 0.0000 450.0000
## 2755 0.0000 450.0000
## 2756 0.0000 450.0000
## 2757 0.0000 450.0000
## 2758 0.0000 450.0000
## 2759 0.0000 450.0000
## 2760 0.0000 450.0000
## 2761 0.0000 2663.3721
## 2762 0.0000 2663.3721
## 2763 0.0000 2663.3721
## 2764 0.0000 2663.3721
## 2765 0.0000 2663.3721
## 2766 0.0000 2663.3721
## 2767 0.0000 2663.3721
## 2768 0.0000 2663.3721
## 2769 0.0000 2663.3721
## 2770 0.0000 2663.3721
## 2771 0.0000 2663.3721
## 2772 0.0000 2663.3721
## 2773 0.0000 2663.3721
## 2774 0.0000 2663.3721
## 2775 0.0000 2663.3721
## 2776 0.0000 2663.3721
## 2777 0.0000 2663.3721
## 2778 0.0000 2663.3721
## 2779 0.0000 2663.3721
## 2780 0.0000 2663.3721
## 2781 0.0000 2663.3721
## 2782 0.0000 2663.3721
## 2783 0.0000 2663.3721
## 2784 0.0000 2663.3721
## 2785 1090.1163 1735.4651
## 2786 1090.1163 1735.4651
## 2787 1090.1163 1735.4651
## 2788 1090.1163 1735.4651
## 2789 1090.1163 1735.4651
## 2790 1090.1163 1735.4651
## 2791 1090.1163 1735.4651
## 2792 1090.1163 1735.4651
## 2793 1090.1163 1735.4651
## 2794 1090.1163 1735.4651
## 2795 1090.1163 1735.4651
## 2796 1090.1163 1735.4651
## 2797 1090.1163 1735.4651
## 2798 1090.1163 1735.4651
## 2799 1090.1163 1735.4651
## 2800 1090.1163 1735.4651
## 2801 1090.1163 1735.4651
## 2802 1090.1163 1735.4651
## 2803 1090.1163 1735.4651
## 2804 1090.1163 1735.4651
## 2805 1090.1163 1735.4651
## 2806 1090.1163 1735.4651
## 2807 1090.1163 1735.4651
## 2808 1090.1163 1735.4651
## 2809 1875.0000 1822.6744
## 2810 1875.0000 1822.6744
## 2811 1875.0000 1822.6744
## 2812 1875.0000 1822.6744
## 2813 1875.0000 1822.6744
## 2814 1875.0000 1822.6744
## 2815 1875.0000 1822.6744
## 2816 1875.0000 1822.6744
## 2817 1875.0000 1822.6744
## 2818 1875.0000 1822.6744
## 2819 1875.0000 1822.6744
## 2820 1875.0000 1822.6744
## 2821 1875.0000 1822.6744
## 2822 1875.0000 1822.6744
## 2823 1875.0000 1822.6744
## 2824 1875.0000 1822.6744
## 2825 1875.0000 1822.6744
## 2826 1875.0000 1822.6744
## 2827 1875.0000 1822.6744
## 2828 1875.0000 1822.6744
## 2829 1875.0000 1822.6744
## 2830 1875.0000 1822.6744
## 2831 1875.0000 1822.6744
## 2832 1875.0000 1822.6744
## 2833 1521.8023 0.0000
## 2834 1521.8023 0.0000
## 2835 1521.8023 0.0000
## 2836 1521.8023 0.0000
## 2837 1521.8023 0.0000
## 2838 1521.8023 0.0000
## 2839 1521.8023 0.0000
## 2840 1521.8023 0.0000
## 2841 1521.8023 0.0000
## 2842 1521.8023 0.0000
## 2843 1521.8023 0.0000
## 2844 1521.8023 0.0000
## 2845 1521.8023 0.0000
## 2846 1521.8023 0.0000
## 2847 1521.8023 0.0000
## 2848 1521.8023 0.0000
## 2849 1521.8023 0.0000
## 2850 1521.8023 0.0000
## 2851 1521.8023 0.0000
## 2852 1521.8023 0.0000
## 2853 1521.8023 0.0000
## 2854 1521.8023 0.0000
## 2855 1521.8023 0.0000
## 2856 1521.8023 0.0000
## 2857 545.0581 453.4884
## 2858 545.0581 453.4884
## 2859 545.0581 453.4884
## 2860 545.0581 453.4884
## 2861 545.0581 453.4884
## 2862 545.0581 453.4884
## 2863 545.0581 453.4884
## 2864 545.0581 453.4884
## 2865 545.0581 453.4884
## 2866 545.0581 453.4884
## 2867 545.0581 453.4884
## 2868 545.0581 453.4884
## 2869 545.0581 453.4884
## 2870 545.0581 453.4884
## 2871 545.0581 453.4884
## 2872 545.0581 453.4884
## 2873 545.0581 453.4884
## 2874 545.0581 453.4884
## 2875 545.0581 453.4884
## 2876 545.0581 453.4884
## 2877 545.0581 453.4884
## 2878 545.0581 453.4884
## 2879 545.0581 453.4884
## 2880 545.0581 453.4884
## 2881 2494.1860 5161.0465
## 2882 2494.1860 5161.0465
## 2883 2494.1860 5161.0465
## 2884 2494.1860 5161.0465
## 2885 2494.1860 5161.0465
## 2886 2494.1860 5161.0465
## 2887 2494.1860 5161.0465
## 2888 2494.1860 5161.0465
## 2889 2494.1860 5161.0465
## 2890 2494.1860 5161.0465
## 2891 2494.1860 5161.0465
## 2892 2494.1860 5161.0465
## 2893 2494.1860 5161.0465
## 2894 2494.1860 5161.0465
## 2895 2494.1860 5161.0465
## 2896 2494.1860 5161.0465
## 2897 2494.1860 5161.0465
## 2898 2494.1860 5161.0465
## 2899 2494.1860 5161.0465
## 2900 2494.1860 5161.0465
## 2901 2494.1860 5161.0465
## 2902 2494.1860 5161.0465
## 2903 2494.1860 5161.0465
## 2904 2494.1860 5161.0465
## 2905 0.0000 102.9070
## 2906 0.0000 102.9070
## 2907 0.0000 102.9070
## 2908 0.0000 102.9070
## 2909 0.0000 102.9070
## 2910 0.0000 102.9070
## 2911 0.0000 102.9070
## 2912 0.0000 102.9070
## 2913 0.0000 102.9070
## 2914 0.0000 102.9070
## 2915 0.0000 102.9070
## 2916 0.0000 102.9070
## 2917 0.0000 102.9070
## 2918 0.0000 102.9070
## 2919 0.0000 102.9070
## 2920 0.0000 102.9070
## 2921 0.0000 102.9070
## 2922 0.0000 102.9070
## 2923 0.0000 102.9070
## 2924 0.0000 102.9070
## 2925 0.0000 102.9070
## 2926 0.0000 102.9070
## 2927 0.0000 102.9070
## 2928 0.0000 102.9070
## 2929 218.0233 1838.3721
## 2930 218.0233 1838.3721
## 2931 218.0233 1838.3721
## 2932 218.0233 1838.3721
## 2933 218.0233 1838.3721
## 2934 218.0233 1838.3721
## 2935 218.0233 1838.3721
## 2936 218.0233 1838.3721
## 2937 218.0233 1838.3721
## 2938 218.0233 1838.3721
## 2939 218.0233 1838.3721
## 2940 218.0233 1838.3721
## 2941 218.0233 1838.3721
## 2942 218.0233 1838.3721
## 2943 218.0233 1838.3721
## 2944 218.0233 1838.3721
## 2945 218.0233 1838.3721
## 2946 218.0233 1838.3721
## 2947 218.0233 1838.3721
## 2948 218.0233 1838.3721
## 2949 218.0233 1838.3721
## 2950 218.0233 1838.3721
## 2951 218.0233 1838.3721
## 2952 218.0233 1838.3721
## 2953 0.0000 1369.1860
## 2954 0.0000 1369.1860
## 2955 0.0000 1369.1860
## 2956 0.0000 1369.1860
## 2957 0.0000 1369.1860
## 2958 0.0000 1369.1860
## 2959 0.0000 1369.1860
## 2960 0.0000 1369.1860
## 2961 0.0000 1369.1860
## 2962 0.0000 1369.1860
## 2963 0.0000 1369.1860
## 2964 0.0000 1369.1860
## 2965 0.0000 1369.1860
## 2966 0.0000 1369.1860
## 2967 0.0000 1369.1860
## 2968 0.0000 1369.1860
## 2969 0.0000 1369.1860
## 2970 0.0000 1369.1860
## 2971 0.0000 1369.1860
## 2972 0.0000 1369.1860
## 2973 0.0000 1369.1860
## 2974 0.0000 1369.1860
## 2975 0.0000 1369.1860
## 2976 0.0000 1369.1860
## 2977 0.0000 2532.5581
## 2978 0.0000 2532.5581
## 2979 0.0000 2532.5581
## 2980 0.0000 2532.5581
## 2981 0.0000 2532.5581
## 2982 0.0000 2532.5581
## 2983 0.0000 2532.5581
## 2984 0.0000 2532.5581
## 2985 0.0000 2532.5581
## 2986 0.0000 2532.5581
## 2987 0.0000 2532.5581
## 2988 0.0000 2532.5581
## 2989 0.0000 2532.5581
## 2990 0.0000 2532.5581
## 2991 0.0000 2532.5581
## 2992 0.0000 2532.5581
## 2993 0.0000 2532.5581
## 2994 0.0000 2532.5581
## 2995 0.0000 2532.5581
## 2996 0.0000 2532.5581
## 2997 0.0000 2532.5581
## 2998 0.0000 2532.5581
## 2999 0.0000 2532.5581
## 3000 0.0000 2532.5581
## 3001 0.0000 392.4419
## 3002 0.0000 392.4419
## 3003 0.0000 392.4419
## 3004 0.0000 392.4419
## 3005 0.0000 392.4419
## 3006 0.0000 392.4419
## 3007 0.0000 392.4419
## 3008 0.0000 392.4419
## 3009 0.0000 392.4419
## 3010 0.0000 392.4419
## 3011 0.0000 392.4419
## 3012 0.0000 392.4419
## 3013 0.0000 392.4419
## 3014 0.0000 392.4419
## 3015 0.0000 392.4419
## 3016 0.0000 392.4419
## 3017 0.0000 392.4419
## 3018 0.0000 392.4419
## 3019 0.0000 392.4419
## 3020 0.0000 392.4419
## 3021 0.0000 392.4419
## 3022 0.0000 392.4419
## 3023 0.0000 392.4419
## 3024 0.0000 392.4419
## 3025 545.0581 213.6628
## 3026 545.0581 213.6628
## 3027 545.0581 213.6628
## 3028 545.0581 213.6628
## 3029 545.0581 213.6628
## 3030 545.0581 213.6628
## 3031 545.0581 213.6628
## 3032 545.0581 213.6628
## 3033 545.0581 213.6628
## 3034 545.0581 213.6628
## 3035 545.0581 213.6628
## 3036 545.0581 213.6628
## 3037 545.0581 213.6628
## 3038 545.0581 213.6628
## 3039 545.0581 213.6628
## 3040 545.0581 213.6628
## 3041 545.0581 213.6628
## 3042 545.0581 213.6628
## 3043 545.0581 213.6628
## 3044 545.0581 213.6628
## 3045 545.0581 213.6628
## 3046 545.0581 213.6628
## 3047 545.0581 213.6628
## 3048 545.0581 213.6628
## 3049 1630.8140 504.0698
## 3050 1630.8140 504.0698
## 3051 1630.8140 504.0698
## 3052 1630.8140 504.0698
## 3053 1630.8140 504.0698
## 3054 1630.8140 504.0698
## 3055 1630.8140 504.0698
## 3056 1630.8140 504.0698
## 3057 1630.8140 504.0698
## 3058 1630.8140 504.0698
## 3059 1630.8140 504.0698
## 3060 1630.8140 504.0698
## 3061 1630.8140 504.0698
## 3062 1630.8140 504.0698
## 3063 1630.8140 504.0698
## 3064 1630.8140 504.0698
## 3065 1630.8140 504.0698
## 3066 1630.8140 504.0698
## 3067 1630.8140 504.0698
## 3068 1630.8140 504.0698
## 3069 1630.8140 504.0698
## 3070 1630.8140 504.0698
## 3071 1630.8140 504.0698
## 3072 1630.8140 504.0698
## 3073 2494.1860 5161.0465
## 3074 2494.1860 5161.0465
## 3075 2494.1860 5161.0465
## 3076 2494.1860 5161.0465
## 3077 2494.1860 5161.0465
## 3078 2494.1860 5161.0465
## 3079 2494.1860 5161.0465
## 3080 2494.1860 5161.0465
## 3081 2494.1860 5161.0465
## 3082 2494.1860 5161.0465
## 3083 2494.1860 5161.0465
## 3084 2494.1860 5161.0465
## 3085 2494.1860 5161.0465
## 3086 2494.1860 5161.0465
## 3087 2494.1860 5161.0465
## 3088 2494.1860 5161.0465
## 3089 2494.1860 5161.0465
## 3090 2494.1860 5161.0465
## 3091 2494.1860 5161.0465
## 3092 2494.1860 5161.0465
## 3093 2494.1860 5161.0465
## 3094 2494.1860 5161.0465
## 3095 2494.1860 5161.0465
## 3096 2494.1860 5161.0465
## 3097 1220.9302 5823.8372
## 3098 1220.9302 5823.8372
## 3099 1220.9302 5823.8372
## 3100 1220.9302 5823.8372
## 3101 1220.9302 5823.8372
## 3102 1220.9302 5823.8372
## 3103 1220.9302 5823.8372
## 3104 1220.9302 5823.8372
## 3105 1220.9302 5823.8372
## 3106 1220.9302 5823.8372
## 3107 1220.9302 5823.8372
## 3108 1220.9302 5823.8372
## 3109 1220.9302 5823.8372
## 3110 1220.9302 5823.8372
## 3111 1220.9302 5823.8372
## 3112 1220.9302 5823.8372
## 3113 1220.9302 5823.8372
## 3114 1220.9302 5823.8372
## 3115 1220.9302 5823.8372
## 3116 1220.9302 5823.8372
## 3117 1220.9302 5823.8372
## 3118 1220.9302 5823.8372
## 3119 1220.9302 5823.8372
## 3120 1220.9302 5823.8372
## 3121 218.0233 198.8372
## 3122 218.0233 198.8372
## 3123 218.0233 198.8372
## 3124 218.0233 198.8372
## 3125 218.0233 198.8372
## 3126 218.0233 198.8372
## 3127 218.0233 198.8372
## 3128 218.0233 198.8372
## 3129 218.0233 198.8372
## 3130 218.0233 198.8372
## 3131 218.0233 198.8372
## 3132 218.0233 198.8372
## 3133 218.0233 198.8372
## 3134 218.0233 198.8372
## 3135 218.0233 198.8372
## 3136 218.0233 198.8372
## 3137 218.0233 198.8372
## 3138 218.0233 198.8372
## 3139 218.0233 198.8372
## 3140 218.0233 198.8372
## 3141 218.0233 198.8372
## 3142 218.0233 198.8372
## 3143 218.0233 198.8372
## 3144 218.0233 198.8372
## 3145 0.0000 3052.3256
## 3146 0.0000 3052.3256
## 3147 0.0000 3052.3256
## 3148 0.0000 3052.3256
## 3149 0.0000 3052.3256
## 3150 0.0000 3052.3256
## 3151 0.0000 3052.3256
## 3152 0.0000 3052.3256
## 3153 0.0000 3052.3256
## 3154 0.0000 3052.3256
## 3155 0.0000 3052.3256
## 3156 0.0000 3052.3256
## 3157 0.0000 3052.3256
## 3158 0.0000 3052.3256
## 3159 0.0000 3052.3256
## 3160 0.0000 3052.3256
## 3161 0.0000 3052.3256
## 3162 0.0000 3052.3256
## 3163 0.0000 3052.3256
## 3164 0.0000 3052.3256
## 3165 0.0000 3052.3256
## 3166 0.0000 3052.3256
## 3167 0.0000 3052.3256
## 3168 0.0000 3052.3256
## 3169 0.0000 1948.2558
## 3170 0.0000 1948.2558
## 3171 0.0000 1948.2558
## 3172 0.0000 1948.2558
## 3173 0.0000 1948.2558
## 3174 0.0000 1948.2558
## 3175 0.0000 1948.2558
## 3176 0.0000 1948.2558
## 3177 0.0000 1948.2558
## 3178 0.0000 1948.2558
## 3179 0.0000 1948.2558
## 3180 0.0000 1948.2558
## 3181 0.0000 1948.2558
## 3182 0.0000 1948.2558
## 3183 0.0000 1948.2558
## 3184 0.0000 1948.2558
## 3185 0.0000 1948.2558
## 3186 0.0000 1948.2558
## 3187 0.0000 1948.2558
## 3188 0.0000 1948.2558
## 3189 0.0000 1948.2558
## 3190 0.0000 1948.2558
## 3191 0.0000 1948.2558
## 3192 0.0000 1948.2558
## 3193 0.0000 1665.6977
## 3194 0.0000 1665.6977
## 3195 0.0000 1665.6977
## 3196 0.0000 1665.6977
## 3197 0.0000 1665.6977
## 3198 0.0000 1665.6977
## 3199 0.0000 1665.6977
## 3200 0.0000 1665.6977
## 3201 0.0000 1665.6977
## 3202 0.0000 1665.6977
## 3203 0.0000 1665.6977
## 3204 0.0000 1665.6977
## 3205 0.0000 1665.6977
## 3206 0.0000 1665.6977
## 3207 0.0000 1665.6977
## 3208 0.0000 1665.6977
## 3209 0.0000 1665.6977
## 3210 0.0000 1665.6977
## 3211 0.0000 1665.6977
## 3212 0.0000 1665.6977
## 3213 0.0000 1665.6977
## 3214 0.0000 1665.6977
## 3215 0.0000 1665.6977
## 3216 0.0000 1665.6977
## 3217 0.0000 1125.0000
## 3218 0.0000 1125.0000
## 3219 0.0000 1125.0000
## 3220 0.0000 1125.0000
## 3221 0.0000 1125.0000
## 3222 0.0000 1125.0000
## 3223 0.0000 1125.0000
## 3224 0.0000 1125.0000
## 3225 0.0000 1125.0000
## total_metaecosystem_Pca_indiv total_metaecosystem_Spi_indiv
## 1 883.43023 635.75581
## 2 883.43023 635.75581
## 3 883.43023 635.75581
## 4 883.43023 635.75581
## 5 883.43023 635.75581
## 6 883.43023 635.75581
## 7 883.43023 635.75581
## 8 883.43023 635.75581
## 9 883.43023 635.75581
## 10 883.43023 635.75581
## 11 883.43023 635.75581
## 12 883.43023 635.75581
## 13 883.43023 635.75581
## 14 883.43023 635.75581
## 15 883.43023 635.75581
## 16 883.43023 635.75581
## 17 883.43023 635.75581
## 18 883.43023 635.75581
## 19 883.43023 635.75581
## 20 883.43023 635.75581
## 21 883.43023 635.75581
## 22 883.43023 635.75581
## 23 883.43023 635.75581
## 24 883.43023 635.75581
## 25 1854.06977 1055.23256
## 26 1854.06977 1055.23256
## 27 1854.06977 1055.23256
## 28 1854.06977 1055.23256
## 29 1854.06977 1055.23256
## 30 1854.06977 1055.23256
## 31 1854.06977 1055.23256
## 32 1854.06977 1055.23256
## 33 1854.06977 1055.23256
## 34 1854.06977 1055.23256
## 35 1854.06977 1055.23256
## 36 1854.06977 1055.23256
## 37 1854.06977 1055.23256
## 38 1854.06977 1055.23256
## 39 1854.06977 1055.23256
## 40 1854.06977 1055.23256
## 41 1854.06977 1055.23256
## 42 1854.06977 1055.23256
## 43 1854.06977 1055.23256
## 44 1854.06977 1055.23256
## 45 1854.06977 1055.23256
## 46 1854.06977 1055.23256
## 47 1854.06977 1055.23256
## 48 1854.06977 1055.23256
## 49 1189.53488 1090.11628
## 50 1189.53488 1090.11628
## 51 1189.53488 1090.11628
## 52 1189.53488 1090.11628
## 53 1189.53488 1090.11628
## 54 1189.53488 1090.11628
## 55 1189.53488 1090.11628
## 56 1189.53488 1090.11628
## 57 1189.53488 1090.11628
## 58 1189.53488 1090.11628
## 59 1189.53488 1090.11628
## 60 1189.53488 1090.11628
## 61 1189.53488 1090.11628
## 62 1189.53488 1090.11628
## 63 1189.53488 1090.11628
## 64 1189.53488 1090.11628
## 65 1189.53488 1090.11628
## 66 1189.53488 1090.11628
## 67 1189.53488 1090.11628
## 68 1189.53488 1090.11628
## 69 1189.53488 1090.11628
## 70 1189.53488 1090.11628
## 71 1189.53488 1090.11628
## 72 1189.53488 1090.11628
## 73 3783.13953 2180.23256
## 74 3783.13953 2180.23256
## 75 3783.13953 2180.23256
## 76 3783.13953 2180.23256
## 77 3783.13953 2180.23256
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## 3095 883.43023 635.75581
## 3096 883.43023 635.75581
## 3097 6005.23256 1029.06977
## 3098 6005.23256 1029.06977
## 3099 6005.23256 1029.06977
## 3100 6005.23256 1029.06977
## 3101 6005.23256 1029.06977
## 3102 6005.23256 1029.06977
## 3103 6005.23256 1029.06977
## 3104 6005.23256 1029.06977
## 3105 6005.23256 1029.06977
## 3106 6005.23256 1029.06977
## 3107 6005.23256 1029.06977
## 3108 6005.23256 1029.06977
## 3109 6005.23256 1029.06977
## 3110 6005.23256 1029.06977
## 3111 6005.23256 1029.06977
## 3112 6005.23256 1029.06977
## 3113 6005.23256 1029.06977
## 3114 6005.23256 1029.06977
## 3115 6005.23256 1029.06977
## 3116 6005.23256 1029.06977
## 3117 6005.23256 1029.06977
## 3118 6005.23256 1029.06977
## 3119 6005.23256 1029.06977
## 3120 6005.23256 1029.06977
## 3121 0.00000 0.00000
## 3122 0.00000 0.00000
## 3123 0.00000 0.00000
## 3124 0.00000 0.00000
## 3125 0.00000 0.00000
## 3126 0.00000 0.00000
## 3127 0.00000 0.00000
## 3128 0.00000 0.00000
## 3129 0.00000 0.00000
## 3130 0.00000 0.00000
## 3131 0.00000 0.00000
## 3132 0.00000 0.00000
## 3133 0.00000 0.00000
## 3134 0.00000 0.00000
## 3135 0.00000 0.00000
## 3136 0.00000 0.00000
## 3137 0.00000 0.00000
## 3138 0.00000 0.00000
## 3139 0.00000 0.00000
## 3140 0.00000 0.00000
## 3141 0.00000 0.00000
## 3142 0.00000 0.00000
## 3143 0.00000 0.00000
## 3144 0.00000 0.00000
## 3145 0.00000 2188.95349
## 3146 0.00000 2188.95349
## 3147 0.00000 2188.95349
## 3148 0.00000 2188.95349
## 3149 0.00000 2188.95349
## 3150 0.00000 2188.95349
## 3151 0.00000 2188.95349
## 3152 0.00000 2188.95349
## 3153 0.00000 2188.95349
## 3154 0.00000 2188.95349
## 3155 0.00000 2188.95349
## 3156 0.00000 2188.95349
## 3157 0.00000 2188.95349
## 3158 0.00000 2188.95349
## 3159 0.00000 2188.95349
## 3160 0.00000 2188.95349
## 3161 0.00000 2188.95349
## 3162 0.00000 2188.95349
## 3163 0.00000 2188.95349
## 3164 0.00000 2188.95349
## 3165 0.00000 2188.95349
## 3166 0.00000 2188.95349
## 3167 0.00000 2188.95349
## 3168 0.00000 2188.95349
## 3169 0.00000 3654.06977
## 3170 0.00000 3654.06977
## 3171 0.00000 3654.06977
## 3172 0.00000 3654.06977
## 3173 0.00000 3654.06977
## 3174 0.00000 3654.06977
## 3175 0.00000 3654.06977
## 3176 0.00000 3654.06977
## 3177 0.00000 3654.06977
## 3178 0.00000 3654.06977
## 3179 0.00000 3654.06977
## 3180 0.00000 3654.06977
## 3181 0.00000 3654.06977
## 3182 0.00000 3654.06977
## 3183 0.00000 3654.06977
## 3184 0.00000 3654.06977
## 3185 0.00000 3654.06977
## 3186 0.00000 3654.06977
## 3187 0.00000 3654.06977
## 3188 0.00000 3654.06977
## 3189 0.00000 3654.06977
## 3190 0.00000 3654.06977
## 3191 0.00000 3654.06977
## 3192 0.00000 3654.06977
## 3193 1030.81395 287.79070
## 3194 1030.81395 287.79070
## 3195 1030.81395 287.79070
## 3196 1030.81395 287.79070
## 3197 1030.81395 287.79070
## 3198 1030.81395 287.79070
## 3199 1030.81395 287.79070
## 3200 1030.81395 287.79070
## 3201 1030.81395 287.79070
## 3202 1030.81395 287.79070
## 3203 1030.81395 287.79070
## 3204 1030.81395 287.79070
## 3205 1030.81395 287.79070
## 3206 1030.81395 287.79070
## 3207 1030.81395 287.79070
## 3208 1030.81395 287.79070
## 3209 1030.81395 287.79070
## 3210 1030.81395 287.79070
## 3211 1030.81395 287.79070
## 3212 1030.81395 287.79070
## 3213 1030.81395 287.79070
## 3214 1030.81395 287.79070
## 3215 1030.81395 287.79070
## 3216 1030.81395 287.79070
## 3217 1360.46512 0.00000
## 3218 1360.46512 0.00000
## 3219 1360.46512 0.00000
## 3220 1360.46512 0.00000
## 3221 1360.46512 0.00000
## 3222 1360.46512 0.00000
## 3223 1360.46512 0.00000
## 3224 1360.46512 0.00000
## 3225 1360.46512 0.00000
## total_metaecosystem_Spi_te_indiv total_metaecosystem_Tet_indiv connection
## 1 981.9767 2243.895 isolated
## 2 981.9767 2243.895 isolated
## 3 981.9767 2243.895 isolated
## 4 981.9767 2243.895 isolated
## 5 981.9767 2243.895 isolated
## 6 981.9767 2243.895 isolated
## 7 981.9767 2243.895 isolated
## 8 981.9767 2243.895 isolated
## 9 981.9767 2243.895 isolated
## 10 981.9767 2243.895 isolated
## 11 981.9767 2243.895 isolated
## 12 981.9767 2243.895 isolated
## 13 981.9767 2243.895 isolated
## 14 981.9767 2243.895 isolated
## 15 981.9767 2243.895 isolated
## 16 981.9767 2243.895 isolated
## 17 981.9767 2243.895 isolated
## 18 981.9767 2243.895 isolated
## 19 981.9767 2243.895 isolated
## 20 981.9767 2243.895 isolated
## 21 981.9767 2243.895 isolated
## 22 981.9767 2243.895 isolated
## 23 981.9767 2243.895 isolated
## 24 981.9767 2243.895 isolated
## 25 1090.1163 0.000 isolated
## 26 1090.1163 0.000 isolated
## 27 1090.1163 0.000 isolated
## 28 1090.1163 0.000 isolated
## 29 1090.1163 0.000 isolated
## 30 1090.1163 0.000 isolated
## 31 1090.1163 0.000 isolated
## 32 1090.1163 0.000 isolated
## 33 1090.1163 0.000 isolated
## 34 1090.1163 0.000 isolated
## 35 1090.1163 0.000 isolated
## 36 1090.1163 0.000 isolated
## 37 1090.1163 0.000 isolated
## 38 1090.1163 0.000 isolated
## 39 1090.1163 0.000 isolated
## 40 1090.1163 0.000 isolated
## 41 1090.1163 0.000 isolated
## 42 1090.1163 0.000 isolated
## 43 1090.1163 0.000 isolated
## 44 1090.1163 0.000 isolated
## 45 1090.1163 0.000 isolated
## 46 1090.1163 0.000 isolated
## 47 1090.1163 0.000 isolated
## 48 1090.1163 0.000 isolated
## 49 2084.3023 0.000 isolated
## 50 2084.3023 0.000 isolated
## 51 2084.3023 0.000 isolated
## 52 2084.3023 0.000 isolated
## 53 2084.3023 0.000 isolated
## 54 2084.3023 0.000 isolated
## 55 2084.3023 0.000 isolated
## 56 2084.3023 0.000 isolated
## 57 2084.3023 0.000 isolated
## 58 2084.3023 0.000 isolated
## 59 2084.3023 0.000 isolated
## 60 2084.3023 0.000 isolated
## 61 2084.3023 0.000 isolated
## 62 2084.3023 0.000 isolated
## 63 2084.3023 0.000 isolated
## 64 2084.3023 0.000 isolated
## 65 2084.3023 0.000 isolated
## 66 2084.3023 0.000 isolated
## 67 2084.3023 0.000 isolated
## 68 2084.3023 0.000 isolated
## 69 2084.3023 0.000 isolated
## 70 2084.3023 0.000 isolated
## 71 2084.3023 0.000 isolated
## 72 2084.3023 0.000 isolated
## 73 2947.6744 0.000 isolated
## 74 2947.6744 0.000 isolated
## 75 2947.6744 0.000 isolated
## 76 2947.6744 0.000 isolated
## 77 2947.6744 0.000 isolated
## 78 2947.6744 0.000 isolated
## 79 2947.6744 0.000 isolated
## 80 2947.6744 0.000 isolated
## 81 2947.6744 0.000 isolated
## 82 2947.6744 0.000 isolated
## 83 2947.6744 0.000 isolated
## 84 2947.6744 0.000 isolated
## 85 2947.6744 0.000 isolated
## 86 2947.6744 0.000 isolated
## 87 2947.6744 0.000 isolated
## 88 2947.6744 0.000 isolated
## 89 2947.6744 0.000 isolated
## 90 2947.6744 0.000 isolated
## 91 2947.6744 0.000 isolated
## 92 2947.6744 0.000 isolated
## 93 2947.6744 0.000 isolated
## 94 2947.6744 0.000 isolated
## 95 2947.6744 0.000 isolated
## 96 2947.6744 0.000 isolated
## 97 1308.1395 0.000 isolated
## 98 1308.1395 0.000 isolated
## 99 1308.1395 0.000 isolated
## 100 1308.1395 0.000 isolated
## 101 1308.1395 0.000 isolated
## 102 1308.1395 0.000 isolated
## 103 1308.1395 0.000 isolated
## 104 1308.1395 0.000 isolated
## 105 1308.1395 0.000 isolated
## 106 1308.1395 0.000 isolated
## 107 1308.1395 0.000 isolated
## 108 1308.1395 0.000 isolated
## 109 1308.1395 0.000 isolated
## 110 1308.1395 0.000 isolated
## 111 1308.1395 0.000 isolated
## 112 1308.1395 0.000 isolated
## 113 1308.1395 0.000 isolated
## 114 1308.1395 0.000 isolated
## 115 1308.1395 0.000 isolated
## 116 1308.1395 0.000 isolated
## 117 1308.1395 0.000 isolated
## 118 1308.1395 0.000 isolated
## 119 1308.1395 0.000 isolated
## 120 1308.1395 0.000 isolated
## 121 767.4419 0.000 isolated
## 122 767.4419 0.000 isolated
## 123 767.4419 0.000 isolated
## 124 767.4419 0.000 isolated
## 125 767.4419 0.000 isolated
## 126 767.4419 0.000 isolated
## 127 767.4419 0.000 isolated
## 128 767.4419 0.000 isolated
## 129 767.4419 0.000 isolated
## 130 767.4419 0.000 isolated
## 131 767.4419 0.000 isolated
## 132 767.4419 0.000 isolated
## 133 767.4419 0.000 isolated
## 134 767.4419 0.000 isolated
## 135 767.4419 0.000 isolated
## 136 767.4419 0.000 isolated
## 137 767.4419 0.000 isolated
## 138 767.4419 0.000 isolated
## 139 767.4419 0.000 isolated
## 140 767.4419 0.000 isolated
## 141 767.4419 0.000 isolated
## 142 767.4419 0.000 isolated
## 143 767.4419 0.000 isolated
## 144 767.4419 0.000 isolated
## 145 1460.7558 0.000 isolated
## 146 1460.7558 0.000 isolated
## 147 1460.7558 0.000 isolated
## 148 1460.7558 0.000 isolated
## 149 1460.7558 0.000 isolated
## 150 1460.7558 0.000 isolated
## 151 1460.7558 0.000 isolated
## 152 1460.7558 0.000 isolated
## 153 1460.7558 0.000 isolated
## 154 1460.7558 0.000 isolated
## 155 1460.7558 0.000 isolated
## 156 1460.7558 0.000 isolated
## 157 1460.7558 0.000 isolated
## 158 1460.7558 0.000 isolated
## 159 1460.7558 0.000 isolated
## 160 1460.7558 0.000 isolated
## 161 1460.7558 0.000 isolated
## 162 1460.7558 0.000 isolated
## 163 1460.7558 0.000 isolated
## 164 1460.7558 0.000 isolated
## 165 1460.7558 0.000 isolated
## 166 1460.7558 0.000 isolated
## 167 1460.7558 0.000 isolated
## 168 1460.7558 0.000 isolated
## 169 187.5000 0.000 isolated
## 170 187.5000 0.000 isolated
## 171 187.5000 0.000 isolated
## 172 187.5000 0.000 isolated
## 173 187.5000 0.000 isolated
## 174 187.5000 0.000 isolated
## 175 187.5000 0.000 isolated
## 176 187.5000 0.000 isolated
## 177 187.5000 0.000 isolated
## 178 187.5000 0.000 isolated
## 179 187.5000 0.000 isolated
## 180 187.5000 0.000 isolated
## 181 187.5000 0.000 isolated
## 182 187.5000 0.000 isolated
## 183 187.5000 0.000 isolated
## 184 187.5000 0.000 isolated
## 185 187.5000 0.000 isolated
## 186 187.5000 0.000 isolated
## 187 187.5000 0.000 isolated
## 188 187.5000 0.000 isolated
## 189 187.5000 0.000 isolated
## 190 187.5000 0.000 isolated
## 191 187.5000 0.000 isolated
## 192 187.5000 0.000 isolated
## 193 981.9767 2243.895 isolated
## 194 981.9767 2243.895 isolated
## 195 981.9767 2243.895 isolated
## 196 981.9767 2243.895 isolated
## 197 981.9767 2243.895 isolated
## 198 981.9767 2243.895 isolated
## 199 981.9767 2243.895 isolated
## 200 981.9767 2243.895 isolated
## 201 981.9767 2243.895 isolated
## 202 981.9767 2243.895 isolated
## 203 981.9767 2243.895 isolated
## 204 981.9767 2243.895 isolated
## 205 981.9767 2243.895 isolated
## 206 981.9767 2243.895 isolated
## 207 981.9767 2243.895 isolated
## 208 981.9767 2243.895 isolated
## 209 981.9767 2243.895 isolated
## 210 981.9767 2243.895 isolated
## 211 981.9767 2243.895 isolated
## 212 981.9767 2243.895 isolated
## 213 981.9767 2243.895 isolated
## 214 981.9767 2243.895 isolated
## 215 981.9767 2243.895 isolated
## 216 981.9767 2243.895 isolated
## 217 0.0000 0.000 isolated
## 218 0.0000 0.000 isolated
## 219 0.0000 0.000 isolated
## 220 0.0000 0.000 isolated
## 221 0.0000 0.000 isolated
## 222 0.0000 0.000 isolated
## 223 0.0000 0.000 isolated
## 224 0.0000 0.000 isolated
## 225 0.0000 0.000 isolated
## 226 0.0000 0.000 isolated
## 227 0.0000 0.000 isolated
## 228 0.0000 0.000 isolated
## 229 0.0000 0.000 isolated
## 230 0.0000 0.000 isolated
## 231 0.0000 0.000 isolated
## 232 0.0000 0.000 isolated
## 233 0.0000 0.000 isolated
## 234 0.0000 0.000 isolated
## 235 0.0000 0.000 isolated
## 236 0.0000 0.000 isolated
## 237 0.0000 0.000 isolated
## 238 0.0000 0.000 isolated
## 239 0.0000 0.000 isolated
## 240 0.0000 0.000 isolated
## 241 1997.0930 0.000 isolated
## 242 1997.0930 0.000 isolated
## 243 1997.0930 0.000 isolated
## 244 1997.0930 0.000 isolated
## 245 1997.0930 0.000 isolated
## 246 1997.0930 0.000 isolated
## 247 1997.0930 0.000 isolated
## 248 1997.0930 0.000 isolated
## 249 1997.0930 0.000 isolated
## 250 1997.0930 0.000 isolated
## 251 1997.0930 0.000 isolated
## 252 1997.0930 0.000 isolated
## 253 1997.0930 0.000 isolated
## 254 1997.0930 0.000 isolated
## 255 1997.0930 0.000 isolated
## 256 1997.0930 0.000 isolated
## 257 1997.0930 0.000 isolated
## 258 1997.0930 0.000 isolated
## 259 1997.0930 0.000 isolated
## 260 1997.0930 0.000 isolated
## 261 1997.0930 0.000 isolated
## 262 1997.0930 0.000 isolated
## 263 1997.0930 0.000 isolated
## 264 1997.0930 0.000 isolated
## 265 3122.0930 0.000 isolated
## 266 3122.0930 0.000 isolated
## 267 3122.0930 0.000 isolated
## 268 3122.0930 0.000 isolated
## 269 3122.0930 0.000 isolated
## 270 3122.0930 0.000 isolated
## 271 3122.0930 0.000 isolated
## 272 3122.0930 0.000 isolated
## 273 3122.0930 0.000 isolated
## 274 3122.0930 0.000 isolated
## 275 3122.0930 0.000 isolated
## 276 3122.0930 0.000 isolated
## 277 3122.0930 0.000 isolated
## 278 3122.0930 0.000 isolated
## 279 3122.0930 0.000 isolated
## 280 3122.0930 0.000 isolated
## 281 3122.0930 0.000 isolated
## 282 3122.0930 0.000 isolated
## 283 3122.0930 0.000 isolated
## 284 3122.0930 0.000 isolated
## 285 3122.0930 0.000 isolated
## 286 3122.0930 0.000 isolated
## 287 3122.0930 0.000 isolated
## 288 3122.0930 0.000 isolated
## 289 218.0233 0.000 isolated
## 290 218.0233 0.000 isolated
## 291 218.0233 0.000 isolated
## 292 218.0233 0.000 isolated
## 293 218.0233 0.000 isolated
## 294 218.0233 0.000 isolated
## 295 218.0233 0.000 isolated
## 296 218.0233 0.000 isolated
## 297 218.0233 0.000 isolated
## 298 218.0233 0.000 isolated
## 299 218.0233 0.000 isolated
## 300 218.0233 0.000 isolated
## 301 218.0233 0.000 isolated
## 302 218.0233 0.000 isolated
## 303 218.0233 0.000 isolated
## 304 218.0233 0.000 isolated
## 305 218.0233 0.000 isolated
## 306 218.0233 0.000 isolated
## 307 218.0233 0.000 isolated
## 308 218.0233 0.000 isolated
## 309 218.0233 0.000 isolated
## 310 218.0233 0.000 isolated
## 311 218.0233 0.000 isolated
## 312 218.0233 0.000 isolated
## 313 3270.3488 0.000 isolated
## 314 3270.3488 0.000 isolated
## 315 3270.3488 0.000 isolated
## 316 3270.3488 0.000 isolated
## 317 3270.3488 0.000 isolated
## 318 3270.3488 0.000 isolated
## 319 3270.3488 0.000 isolated
## 320 3270.3488 0.000 isolated
## 321 3270.3488 0.000 isolated
## 322 3270.3488 0.000 isolated
## 323 3270.3488 0.000 isolated
## 324 3270.3488 0.000 isolated
## 325 3270.3488 0.000 isolated
## 326 3270.3488 0.000 isolated
## 327 3270.3488 0.000 isolated
## 328 3270.3488 0.000 isolated
## 329 3270.3488 0.000 isolated
## 330 3270.3488 0.000 isolated
## 331 3270.3488 0.000 isolated
## 332 3270.3488 0.000 isolated
## 333 3270.3488 0.000 isolated
## 334 3270.3488 0.000 isolated
## 335 3270.3488 0.000 isolated
## 336 3270.3488 0.000 isolated
## 337 218.0233 0.000 isolated
## 338 218.0233 0.000 isolated
## 339 218.0233 0.000 isolated
## 340 218.0233 0.000 isolated
## 341 218.0233 0.000 isolated
## 342 218.0233 0.000 isolated
## 343 218.0233 0.000 isolated
## 344 218.0233 0.000 isolated
## 345 218.0233 0.000 isolated
## 346 218.0233 0.000 isolated
## 347 218.0233 0.000 isolated
## 348 218.0233 0.000 isolated
## 349 218.0233 0.000 isolated
## 350 218.0233 0.000 isolated
## 351 218.0233 0.000 isolated
## 352 218.0233 0.000 isolated
## 353 218.0233 0.000 isolated
## 354 218.0233 0.000 isolated
## 355 218.0233 0.000 isolated
## 356 218.0233 0.000 isolated
## 357 218.0233 0.000 isolated
## 358 218.0233 0.000 isolated
## 359 218.0233 0.000 isolated
## 360 218.0233 0.000 isolated
## 361 928.7791 0.000 isolated
## 362 928.7791 0.000 isolated
## 363 928.7791 0.000 isolated
## 364 928.7791 0.000 isolated
## 365 928.7791 0.000 isolated
## 366 928.7791 0.000 isolated
## 367 928.7791 0.000 isolated
## 368 928.7791 0.000 isolated
## 369 928.7791 0.000 isolated
## 370 928.7791 0.000 isolated
## 371 928.7791 0.000 isolated
## 372 928.7791 0.000 isolated
## 373 928.7791 0.000 isolated
## 374 928.7791 0.000 isolated
## 375 928.7791 0.000 isolated
## 376 928.7791 0.000 isolated
## 377 928.7791 0.000 isolated
## 378 928.7791 0.000 isolated
## 379 928.7791 0.000 isolated
## 380 928.7791 0.000 isolated
## 381 928.7791 0.000 isolated
## 382 928.7791 0.000 isolated
## 383 928.7791 0.000 isolated
## 384 928.7791 0.000 isolated
## 385 981.9767 2243.895 isolated
## 386 981.9767 2243.895 isolated
## 387 981.9767 2243.895 isolated
## 388 981.9767 2243.895 isolated
## 389 981.9767 2243.895 isolated
## 390 981.9767 2243.895 isolated
## 391 981.9767 2243.895 isolated
## 392 981.9767 2243.895 isolated
## 393 981.9767 2243.895 isolated
## 394 981.9767 2243.895 isolated
## 395 981.9767 2243.895 isolated
## 396 981.9767 2243.895 isolated
## 397 981.9767 2243.895 isolated
## 398 981.9767 2243.895 isolated
## 399 981.9767 2243.895 isolated
## 400 981.9767 2243.895 isolated
## 401 981.9767 2243.895 isolated
## 402 981.9767 2243.895 isolated
## 403 981.9767 2243.895 isolated
## 404 981.9767 2243.895 isolated
## 405 981.9767 2243.895 isolated
## 406 981.9767 2243.895 isolated
## 407 981.9767 2243.895 isolated
## 408 981.9767 2243.895 isolated
## 409 2825.5814 0.000 isolated
## 410 2825.5814 0.000 isolated
## 411 2825.5814 0.000 isolated
## 412 2825.5814 0.000 isolated
## 413 2825.5814 0.000 isolated
## 414 2825.5814 0.000 isolated
## 415 2825.5814 0.000 isolated
## 416 2825.5814 0.000 isolated
## 417 2825.5814 0.000 isolated
## 418 2825.5814 0.000 isolated
## 419 2825.5814 0.000 isolated
## 420 2825.5814 0.000 isolated
## 421 2825.5814 0.000 isolated
## 422 2825.5814 0.000 isolated
## 423 2825.5814 0.000 isolated
## 424 2825.5814 0.000 isolated
## 425 2825.5814 0.000 isolated
## 426 2825.5814 0.000 isolated
## 427 2825.5814 0.000 isolated
## 428 2825.5814 0.000 isolated
## 429 2825.5814 0.000 isolated
## 430 2825.5814 0.000 isolated
## 431 2825.5814 0.000 isolated
## 432 2825.5814 0.000 isolated
## 433 3174.4186 0.000 isolated
## 434 3174.4186 0.000 isolated
## 435 3174.4186 0.000 isolated
## 436 3174.4186 0.000 isolated
## 437 3174.4186 0.000 isolated
## 438 3174.4186 0.000 isolated
## 439 3174.4186 0.000 isolated
## 440 3174.4186 0.000 isolated
## 441 3174.4186 0.000 isolated
## 442 3174.4186 0.000 isolated
## 443 3174.4186 0.000 isolated
## 444 3174.4186 0.000 isolated
## 445 3174.4186 0.000 isolated
## 446 3174.4186 0.000 isolated
## 447 3174.4186 0.000 isolated
## 448 3174.4186 0.000 isolated
## 449 3174.4186 0.000 isolated
## 450 3174.4186 0.000 isolated
## 451 3174.4186 0.000 isolated
## 452 3174.4186 0.000 isolated
## 453 3174.4186 0.000 isolated
## 454 3174.4186 0.000 isolated
## 455 3174.4186 0.000 isolated
## 456 3174.4186 0.000 isolated
## 457 6793.6047 0.000 isolated
## 458 6793.6047 0.000 isolated
## 459 6793.6047 0.000 isolated
## 460 6793.6047 0.000 isolated
## 461 6793.6047 0.000 isolated
## 462 6793.6047 0.000 isolated
## 463 6793.6047 0.000 isolated
## 464 6793.6047 0.000 isolated
## 465 6793.6047 0.000 isolated
## 466 6793.6047 0.000 isolated
## 467 6793.6047 0.000 isolated
## 468 6793.6047 0.000 isolated
## 469 6793.6047 0.000 isolated
## 470 6793.6047 0.000 isolated
## 471 6793.6047 0.000 isolated
## 472 6793.6047 0.000 isolated
## 473 6793.6047 0.000 isolated
## 474 6793.6047 0.000 isolated
## 475 6793.6047 0.000 isolated
## 476 6793.6047 0.000 isolated
## 477 6793.6047 0.000 isolated
## 478 6793.6047 0.000 isolated
## 479 6793.6047 0.000 isolated
## 480 6793.6047 0.000 isolated
## 481 2433.1395 0.000 isolated
## 482 2433.1395 0.000 isolated
## 483 2433.1395 0.000 isolated
## 484 2433.1395 0.000 isolated
## 485 2433.1395 0.000 isolated
## 486 2433.1395 0.000 isolated
## 487 2433.1395 0.000 isolated
## 488 2433.1395 0.000 isolated
## 489 2433.1395 0.000 isolated
## 490 2433.1395 0.000 isolated
## 491 2433.1395 0.000 isolated
## 492 2433.1395 0.000 isolated
## 493 2433.1395 0.000 isolated
## 494 2433.1395 0.000 isolated
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## 1677 0.0000 0.000 isolated
## 1678 0.0000 0.000 isolated
## 1679 0.0000 0.000 isolated
## 1680 0.0000 0.000 isolated
## 1681 0.0000 0.000 isolated
## 1682 0.0000 0.000 isolated
## 1683 0.0000 0.000 isolated
## 1684 0.0000 0.000 isolated
## 1685 0.0000 0.000 isolated
## 1686 0.0000 0.000 isolated
## 1687 0.0000 0.000 isolated
## 1688 0.0000 0.000 isolated
## 1689 0.0000 0.000 isolated
## 1690 0.0000 0.000 isolated
## 1691 0.0000 0.000 isolated
## 1692 0.0000 0.000 isolated
## 1693 0.0000 0.000 isolated
## 1694 0.0000 0.000 isolated
## 1695 0.0000 0.000 isolated
## 1696 0.0000 0.000 isolated
## 1697 0.0000 0.000 isolated
## 1698 0.0000 0.000 isolated
## 1699 0.0000 0.000 isolated
## 1700 0.0000 0.000 isolated
## 1701 0.0000 0.000 isolated
## 1702 0.0000 0.000 isolated
## 1703 0.0000 0.000 isolated
## 1704 0.0000 0.000 isolated
## 1705 0.0000 0.000 isolated
## 1706 0.0000 0.000 isolated
## 1707 0.0000 0.000 isolated
## 1708 0.0000 0.000 isolated
## 1709 0.0000 0.000 isolated
## 1710 0.0000 0.000 isolated
## 1711 0.0000 0.000 isolated
## 1712 0.0000 0.000 isolated
## 1713 0.0000 0.000 isolated
## 1714 0.0000 0.000 isolated
## 1715 0.0000 0.000 isolated
## 1716 0.0000 0.000 isolated
## 1717 0.0000 0.000 isolated
## 1718 0.0000 0.000 isolated
## 1719 0.0000 0.000 isolated
## 1720 0.0000 0.000 isolated
## 1721 0.0000 0.000 isolated
## 1722 0.0000 0.000 isolated
## 1723 0.0000 0.000 isolated
## 1724 0.0000 0.000 isolated
## 1725 0.0000 0.000 isolated
## 1726 0.0000 0.000 isolated
## 1727 0.0000 0.000 isolated
## 1728 0.0000 0.000 isolated
## 1729 981.9767 2243.895 isolated
## 1730 981.9767 2243.895 isolated
## 1731 981.9767 2243.895 isolated
## 1732 981.9767 2243.895 isolated
## 1733 981.9767 2243.895 isolated
## 1734 981.9767 2243.895 isolated
## 1735 981.9767 2243.895 isolated
## 1736 981.9767 2243.895 isolated
## 1737 981.9767 2243.895 isolated
## 1738 981.9767 2243.895 isolated
## 1739 981.9767 2243.895 isolated
## 1740 981.9767 2243.895 isolated
## 1741 981.9767 2243.895 isolated
## 1742 981.9767 2243.895 isolated
## 1743 981.9767 2243.895 isolated
## 1744 981.9767 2243.895 isolated
## 1745 981.9767 2243.895 isolated
## 1746 981.9767 2243.895 isolated
## 1747 981.9767 2243.895 isolated
## 1748 981.9767 2243.895 isolated
## 1749 981.9767 2243.895 isolated
## 1750 981.9767 2243.895 isolated
## 1751 981.9767 2243.895 isolated
## 1752 981.9767 2243.895 isolated
## 1753 1622.0930 0.000 isolated
## 1754 1622.0930 0.000 isolated
## 1755 1622.0930 0.000 isolated
## 1756 1622.0930 0.000 isolated
## 1757 1622.0930 0.000 isolated
## 1758 1622.0930 0.000 isolated
## 1759 1622.0930 0.000 isolated
## 1760 1622.0930 0.000 isolated
## 1761 1622.0930 0.000 isolated
## 1762 1622.0930 0.000 isolated
## 1763 1622.0930 0.000 isolated
## 1764 1622.0930 0.000 isolated
## 1765 1622.0930 0.000 isolated
## 1766 1622.0930 0.000 isolated
## 1767 1622.0930 0.000 isolated
## 1768 1622.0930 0.000 isolated
## 1769 1622.0930 0.000 isolated
## 1770 1622.0930 0.000 isolated
## 1771 1622.0930 0.000 isolated
## 1772 1622.0930 0.000 isolated
## 1773 1622.0930 0.000 isolated
## 1774 1622.0930 0.000 isolated
## 1775 1622.0930 0.000 isolated
## 1776 1622.0930 0.000 isolated
## 1777 1613.3721 0.000 isolated
## 1778 1613.3721 0.000 isolated
## 1779 1613.3721 0.000 isolated
## 1780 1613.3721 0.000 isolated
## 1781 1613.3721 0.000 isolated
## 1782 1613.3721 0.000 isolated
## 1783 1613.3721 0.000 isolated
## 1784 1613.3721 0.000 isolated
## 1785 1613.3721 0.000 isolated
## 1786 1613.3721 0.000 isolated
## 1787 1613.3721 0.000 isolated
## 1788 1613.3721 0.000 isolated
## 1789 1613.3721 0.000 isolated
## 1790 1613.3721 0.000 isolated
## 1791 1613.3721 0.000 isolated
## 1792 1613.3721 0.000 isolated
## 1793 1613.3721 0.000 isolated
## 1794 1613.3721 0.000 isolated
## 1795 1613.3721 0.000 isolated
## 1796 1613.3721 0.000 isolated
## 1797 1613.3721 0.000 isolated
## 1798 1613.3721 0.000 isolated
## 1799 1613.3721 0.000 isolated
## 1800 1613.3721 0.000 isolated
## 1801 2136.6279 0.000 isolated
## 1802 2136.6279 0.000 isolated
## 1803 2136.6279 0.000 isolated
## 1804 2136.6279 0.000 isolated
## 1805 2136.6279 0.000 isolated
## 1806 2136.6279 0.000 isolated
## 1807 2136.6279 0.000 isolated
## 1808 2136.6279 0.000 isolated
## 1809 2136.6279 0.000 isolated
## 1810 2136.6279 0.000 isolated
## 1811 2136.6279 0.000 isolated
## 1812 2136.6279 0.000 isolated
## 1813 2136.6279 0.000 isolated
## 1814 2136.6279 0.000 isolated
## 1815 2136.6279 0.000 isolated
## 1816 2136.6279 0.000 isolated
## 1817 2136.6279 0.000 isolated
## 1818 2136.6279 0.000 isolated
## 1819 2136.6279 0.000 isolated
## 1820 2136.6279 0.000 isolated
## 1821 2136.6279 0.000 isolated
## 1822 2136.6279 0.000 isolated
## 1823 2136.6279 0.000 isolated
## 1824 2136.6279 0.000 isolated
## 1825 1090.1163 0.000 isolated
## 1826 1090.1163 0.000 isolated
## 1827 1090.1163 0.000 isolated
## 1828 1090.1163 0.000 isolated
## 1829 1090.1163 0.000 isolated
## 1830 1090.1163 0.000 isolated
## 1831 1090.1163 0.000 isolated
## 1832 1090.1163 0.000 isolated
## 1833 1090.1163 0.000 isolated
## 1834 1090.1163 0.000 isolated
## 1835 1090.1163 0.000 isolated
## 1836 1090.1163 0.000 isolated
## 1837 1090.1163 0.000 isolated
## 1838 1090.1163 0.000 isolated
## 1839 1090.1163 0.000 isolated
## 1840 1090.1163 0.000 isolated
## 1841 1090.1163 0.000 isolated
## 1842 1090.1163 0.000 isolated
## 1843 1090.1163 0.000 isolated
## 1844 1090.1163 0.000 isolated
## 1845 1090.1163 0.000 isolated
## 1846 1090.1163 0.000 isolated
## 1847 1090.1163 0.000 isolated
## 1848 1090.1163 0.000 isolated
## 1849 1090.1163 0.000 isolated
## 1850 1090.1163 0.000 isolated
## 1851 1090.1163 0.000 isolated
## 1852 1090.1163 0.000 isolated
## 1853 1090.1163 0.000 isolated
## 1854 1090.1163 0.000 isolated
## 1855 1090.1163 0.000 isolated
## 1856 1090.1163 0.000 isolated
## 1857 1090.1163 0.000 isolated
## 1858 1090.1163 0.000 isolated
## 1859 1090.1163 0.000 isolated
## 1860 1090.1163 0.000 isolated
## 1861 1090.1163 0.000 isolated
## 1862 1090.1163 0.000 isolated
## 1863 1090.1163 0.000 isolated
## 1864 1090.1163 0.000 isolated
## 1865 1090.1163 0.000 isolated
## 1866 1090.1163 0.000 isolated
## 1867 1090.1163 0.000 isolated
## 1868 1090.1163 0.000 isolated
## 1869 1090.1163 0.000 isolated
## 1870 1090.1163 0.000 isolated
## 1871 1090.1163 0.000 isolated
## 1872 1090.1163 0.000 isolated
## 1873 3126.4535 0.000 isolated
## 1874 3126.4535 0.000 isolated
## 1875 3126.4535 0.000 isolated
## 1876 3126.4535 0.000 isolated
## 1877 3126.4535 0.000 isolated
## 1878 3126.4535 0.000 isolated
## 1879 3126.4535 0.000 isolated
## 1880 3126.4535 0.000 isolated
## 1881 3126.4535 0.000 isolated
## 1882 3126.4535 0.000 isolated
## 1883 3126.4535 0.000 isolated
## 1884 3126.4535 0.000 isolated
## 1885 3126.4535 0.000 isolated
## 1886 3126.4535 0.000 isolated
## 1887 3126.4535 0.000 isolated
## 1888 3126.4535 0.000 isolated
## 1889 3126.4535 0.000 isolated
## 1890 3126.4535 0.000 isolated
## 1891 3126.4535 0.000 isolated
## 1892 3126.4535 0.000 isolated
## 1893 3126.4535 0.000 isolated
## 1894 3126.4535 0.000 isolated
## 1895 3126.4535 0.000 isolated
## 1896 3126.4535 0.000 isolated
## 1897 1077.0349 0.000 isolated
## 1898 1077.0349 0.000 isolated
## 1899 1077.0349 0.000 isolated
## 1900 1077.0349 0.000 isolated
## 1901 1077.0349 0.000 isolated
## 1902 1077.0349 0.000 isolated
## 1903 1077.0349 0.000 isolated
## 1904 1077.0349 0.000 isolated
## 1905 1077.0349 0.000 isolated
## 1906 1077.0349 0.000 isolated
## 1907 1077.0349 0.000 isolated
## 1908 1077.0349 0.000 isolated
## 1909 1077.0349 0.000 isolated
## 1910 1077.0349 0.000 isolated
## 1911 1077.0349 0.000 isolated
## 1912 1077.0349 0.000 isolated
## 1913 1077.0349 0.000 isolated
## 1914 1077.0349 0.000 isolated
## 1915 1077.0349 0.000 isolated
## 1916 1077.0349 0.000 isolated
## 1917 1077.0349 0.000 isolated
## 1918 1077.0349 0.000 isolated
## 1919 1077.0349 0.000 isolated
## 1920 1077.0349 0.000 isolated
## 1921 981.9767 2243.895 isolated
## 1922 981.9767 2243.895 isolated
## 1923 981.9767 2243.895 isolated
## 1924 981.9767 2243.895 isolated
## 1925 981.9767 2243.895 isolated
## 1926 981.9767 2243.895 isolated
## 1927 981.9767 2243.895 isolated
## 1928 981.9767 2243.895 isolated
## 1929 981.9767 2243.895 isolated
## 1930 981.9767 2243.895 isolated
## 1931 981.9767 2243.895 isolated
## 1932 981.9767 2243.895 isolated
## 1933 981.9767 2243.895 isolated
## 1934 981.9767 2243.895 isolated
## 1935 981.9767 2243.895 isolated
## 1936 981.9767 2243.895 isolated
## 1937 981.9767 2243.895 isolated
## 1938 981.9767 2243.895 isolated
## 1939 981.9767 2243.895 isolated
## 1940 981.9767 2243.895 isolated
## 1941 981.9767 2243.895 isolated
## 1942 981.9767 2243.895 isolated
## 1943 981.9767 2243.895 isolated
## 1944 981.9767 2243.895 isolated
## 1945 1526.1628 0.000 isolated
## 1946 1526.1628 0.000 isolated
## 1947 1526.1628 0.000 isolated
## 1948 1526.1628 0.000 isolated
## 1949 1526.1628 0.000 isolated
## 1950 1526.1628 0.000 isolated
## 1951 1526.1628 0.000 isolated
## 1952 1526.1628 0.000 isolated
## 1953 1526.1628 0.000 isolated
## 1954 1526.1628 0.000 isolated
## 1955 1526.1628 0.000 isolated
## 1956 1526.1628 0.000 isolated
## 1957 1526.1628 0.000 isolated
## 1958 1526.1628 0.000 isolated
## 1959 1526.1628 0.000 isolated
## 1960 1526.1628 0.000 isolated
## 1961 1526.1628 0.000 isolated
## 1962 1526.1628 0.000 isolated
## 1963 1526.1628 0.000 isolated
## 1964 1526.1628 0.000 isolated
## 1965 1526.1628 0.000 isolated
## 1966 1526.1628 0.000 isolated
## 1967 1526.1628 0.000 isolated
## 1968 1526.1628 0.000 isolated
## 1969 1793.0233 0.000 isolated
## 1970 1793.0233 0.000 isolated
## 1971 1793.0233 0.000 isolated
## 1972 1793.0233 0.000 isolated
## 1973 1793.0233 0.000 isolated
## 1974 1793.0233 0.000 isolated
## 1975 1793.0233 0.000 isolated
## 1976 1793.0233 0.000 isolated
## 1977 1793.0233 0.000 isolated
## 1978 1793.0233 0.000 isolated
## 1979 1793.0233 0.000 isolated
## 1980 1793.0233 0.000 isolated
## 1981 1793.0233 0.000 isolated
## 1982 1793.0233 0.000 isolated
## 1983 1793.0233 0.000 isolated
## 1984 1793.0233 0.000 isolated
## 1985 1793.0233 0.000 isolated
## 1986 1793.0233 0.000 isolated
## 1987 1793.0233 0.000 isolated
## 1988 1793.0233 0.000 isolated
## 1989 1793.0233 0.000 isolated
## 1990 1793.0233 0.000 isolated
## 1991 1793.0233 0.000 isolated
## 1992 1793.0233 0.000 isolated
## 1993 3165.6977 0.000 isolated
## 1994 3165.6977 0.000 isolated
## 1995 3165.6977 0.000 isolated
## 1996 3165.6977 0.000 isolated
## 1997 3165.6977 0.000 isolated
## 1998 3165.6977 0.000 isolated
## 1999 3165.6977 0.000 isolated
## 2000 3165.6977 0.000 isolated
## 2001 3165.6977 0.000 isolated
## 2002 3165.6977 0.000 isolated
## 2003 3165.6977 0.000 isolated
## 2004 3165.6977 0.000 isolated
## 2005 3165.6977 0.000 isolated
## 2006 3165.6977 0.000 isolated
## 2007 3165.6977 0.000 isolated
## 2008 3165.6977 0.000 isolated
## 2009 3165.6977 0.000 isolated
## 2010 3165.6977 0.000 isolated
## 2011 3165.6977 0.000 isolated
## 2012 3165.6977 0.000 isolated
## 2013 3165.6977 0.000 isolated
## 2014 3165.6977 0.000 isolated
## 2015 3165.6977 0.000 isolated
## 2016 3165.6977 0.000 isolated
## 2017 1090.1163 0.000 isolated
## 2018 1090.1163 0.000 isolated
## 2019 1090.1163 0.000 isolated
## 2020 1090.1163 0.000 isolated
## 2021 1090.1163 0.000 isolated
## 2022 1090.1163 0.000 isolated
## 2023 1090.1163 0.000 isolated
## 2024 1090.1163 0.000 isolated
## 2025 1090.1163 0.000 isolated
## 2026 1090.1163 0.000 isolated
## 2027 1090.1163 0.000 isolated
## 2028 1090.1163 0.000 isolated
## 2029 1090.1163 0.000 isolated
## 2030 1090.1163 0.000 isolated
## 2031 1090.1163 0.000 isolated
## 2032 1090.1163 0.000 isolated
## 2033 1090.1163 0.000 isolated
## 2034 1090.1163 0.000 isolated
## 2035 1090.1163 0.000 isolated
## 2036 1090.1163 0.000 isolated
## 2037 1090.1163 0.000 isolated
## 2038 1090.1163 0.000 isolated
## 2039 1090.1163 0.000 isolated
## 2040 1090.1163 0.000 isolated
## 2041 767.4419 0.000 isolated
## 2042 767.4419 0.000 isolated
## 2043 767.4419 0.000 isolated
## 2044 767.4419 0.000 isolated
## 2045 767.4419 0.000 isolated
## 2046 767.4419 0.000 isolated
## 2047 767.4419 0.000 isolated
## 2048 767.4419 0.000 isolated
## 2049 767.4419 0.000 isolated
## 2050 767.4419 0.000 isolated
## 2051 767.4419 0.000 isolated
## 2052 767.4419 0.000 isolated
## 2053 767.4419 0.000 isolated
## 2054 767.4419 0.000 isolated
## 2055 767.4419 0.000 isolated
## 2056 767.4419 0.000 isolated
## 2057 767.4419 0.000 isolated
## 2058 767.4419 0.000 isolated
## 2059 767.4419 0.000 isolated
## 2060 767.4419 0.000 isolated
## 2061 767.4419 0.000 isolated
## 2062 767.4419 0.000 isolated
## 2063 767.4419 0.000 isolated
## 2064 767.4419 0.000 isolated
## 2065 1460.7558 0.000 isolated
## 2066 1460.7558 0.000 isolated
## 2067 1460.7558 0.000 isolated
## 2068 1460.7558 0.000 isolated
## 2069 1460.7558 0.000 isolated
## 2070 1460.7558 0.000 isolated
## 2071 1460.7558 0.000 isolated
## 2072 1460.7558 0.000 isolated
## 2073 1460.7558 0.000 isolated
## 2074 1460.7558 0.000 isolated
## 2075 1460.7558 0.000 isolated
## 2076 1460.7558 0.000 isolated
## 2077 1460.7558 0.000 isolated
## 2078 1460.7558 0.000 isolated
## 2079 1460.7558 0.000 isolated
## 2080 1460.7558 0.000 isolated
## 2081 1460.7558 0.000 isolated
## 2082 1460.7558 0.000 isolated
## 2083 1460.7558 0.000 isolated
## 2084 1460.7558 0.000 isolated
## 2085 1460.7558 0.000 isolated
## 2086 1460.7558 0.000 isolated
## 2087 1460.7558 0.000 isolated
## 2088 1460.7558 0.000 isolated
## 2089 187.5000 0.000 isolated
## 2090 187.5000 0.000 isolated
## 2091 187.5000 0.000 isolated
## 2092 187.5000 0.000 isolated
## 2093 187.5000 0.000 isolated
## 2094 187.5000 0.000 isolated
## 2095 187.5000 0.000 isolated
## 2096 187.5000 0.000 isolated
## 2097 187.5000 0.000 isolated
## 2098 187.5000 0.000 isolated
## 2099 187.5000 0.000 isolated
## 2100 187.5000 0.000 isolated
## 2101 187.5000 0.000 isolated
## 2102 187.5000 0.000 isolated
## 2103 187.5000 0.000 isolated
## 2104 187.5000 0.000 isolated
## 2105 187.5000 0.000 isolated
## 2106 187.5000 0.000 isolated
## 2107 187.5000 0.000 isolated
## 2108 187.5000 0.000 isolated
## 2109 187.5000 0.000 isolated
## 2110 187.5000 0.000 isolated
## 2111 187.5000 0.000 isolated
## 2112 187.5000 0.000 isolated
## 2113 981.9767 2243.895 isolated
## 2114 981.9767 2243.895 isolated
## 2115 981.9767 2243.895 isolated
## 2116 981.9767 2243.895 isolated
## 2117 981.9767 2243.895 isolated
## 2118 981.9767 2243.895 isolated
## 2119 981.9767 2243.895 isolated
## 2120 981.9767 2243.895 isolated
## 2121 981.9767 2243.895 isolated
## 2122 981.9767 2243.895 isolated
## 2123 981.9767 2243.895 isolated
## 2124 981.9767 2243.895 isolated
## 2125 981.9767 2243.895 isolated
## 2126 981.9767 2243.895 isolated
## 2127 981.9767 2243.895 isolated
## 2128 981.9767 2243.895 isolated
## 2129 981.9767 2243.895 isolated
## 2130 981.9767 2243.895 isolated
## 2131 981.9767 2243.895 isolated
## 2132 981.9767 2243.895 isolated
## 2133 981.9767 2243.895 isolated
## 2134 981.9767 2243.895 isolated
## 2135 981.9767 2243.895 isolated
## 2136 981.9767 2243.895 isolated
## 2137 436.0465 0.000 isolated
## 2138 436.0465 0.000 isolated
## 2139 436.0465 0.000 isolated
## 2140 436.0465 0.000 isolated
## 2141 436.0465 0.000 isolated
## 2142 436.0465 0.000 isolated
## 2143 436.0465 0.000 isolated
## 2144 436.0465 0.000 isolated
## 2145 436.0465 0.000 isolated
## 2146 436.0465 0.000 isolated
## 2147 436.0465 0.000 isolated
## 2148 436.0465 0.000 isolated
## 2149 436.0465 0.000 isolated
## 2150 436.0465 0.000 isolated
## 2151 436.0465 0.000 isolated
## 2152 436.0465 0.000 isolated
## 2153 436.0465 0.000 isolated
## 2154 436.0465 0.000 isolated
## 2155 436.0465 0.000 isolated
## 2156 436.0465 0.000 isolated
## 2157 436.0465 0.000 isolated
## 2158 436.0465 0.000 isolated
## 2159 436.0465 0.000 isolated
## 2160 436.0465 0.000 isolated
## 2161 1705.8140 0.000 isolated
## 2162 1705.8140 0.000 isolated
## 2163 1705.8140 0.000 isolated
## 2164 1705.8140 0.000 isolated
## 2165 1705.8140 0.000 isolated
## 2166 1705.8140 0.000 isolated
## 2167 1705.8140 0.000 isolated
## 2168 1705.8140 0.000 isolated
## 2169 1705.8140 0.000 isolated
## 2170 1705.8140 0.000 isolated
## 2171 1705.8140 0.000 isolated
## 2172 1705.8140 0.000 isolated
## 2173 1705.8140 0.000 isolated
## 2174 1705.8140 0.000 isolated
## 2175 1705.8140 0.000 isolated
## 2176 1705.8140 0.000 isolated
## 2177 1705.8140 0.000 isolated
## 2178 1705.8140 0.000 isolated
## 2179 1705.8140 0.000 isolated
## 2180 1705.8140 0.000 isolated
## 2181 1705.8140 0.000 isolated
## 2182 1705.8140 0.000 isolated
## 2183 1705.8140 0.000 isolated
## 2184 1705.8140 0.000 isolated
## 2185 3340.1163 0.000 isolated
## 2186 3340.1163 0.000 isolated
## 2187 3340.1163 0.000 isolated
## 2188 3340.1163 0.000 isolated
## 2189 3340.1163 0.000 isolated
## 2190 3340.1163 0.000 isolated
## 2191 3340.1163 0.000 isolated
## 2192 3340.1163 0.000 isolated
## 2193 3340.1163 0.000 isolated
## 2194 3340.1163 0.000 isolated
## 2195 3340.1163 0.000 isolated
## 2196 3340.1163 0.000 isolated
## 2197 3340.1163 0.000 isolated
## 2198 3340.1163 0.000 isolated
## 2199 3340.1163 0.000 isolated
## 2200 3340.1163 0.000 isolated
## 2201 3340.1163 0.000 isolated
## 2202 3340.1163 0.000 isolated
## 2203 3340.1163 0.000 isolated
## 2204 3340.1163 0.000 isolated
## 2205 3340.1163 0.000 isolated
## 2206 3340.1163 0.000 isolated
## 2207 3340.1163 0.000 isolated
## 2208 3340.1163 0.000 isolated
## 2209 0.0000 0.000 isolated
## 2210 0.0000 0.000 isolated
## 2211 0.0000 0.000 isolated
## 2212 0.0000 0.000 isolated
## 2213 0.0000 0.000 isolated
## 2214 0.0000 0.000 isolated
## 2215 0.0000 0.000 isolated
## 2216 0.0000 0.000 isolated
## 2217 0.0000 0.000 isolated
## 2218 0.0000 0.000 isolated
## 2219 0.0000 0.000 isolated
## 2220 0.0000 0.000 isolated
## 2221 0.0000 0.000 isolated
## 2222 0.0000 0.000 isolated
## 2223 0.0000 0.000 isolated
## 2224 0.0000 0.000 isolated
## 2225 0.0000 0.000 isolated
## 2226 0.0000 0.000 isolated
## 2227 0.0000 0.000 isolated
## 2228 0.0000 0.000 isolated
## 2229 0.0000 0.000 isolated
## 2230 0.0000 0.000 isolated
## 2231 0.0000 0.000 isolated
## 2232 0.0000 0.000 isolated
## 2233 3270.3488 0.000 isolated
## 2234 3270.3488 0.000 isolated
## 2235 3270.3488 0.000 isolated
## 2236 3270.3488 0.000 isolated
## 2237 3270.3488 0.000 isolated
## 2238 3270.3488 0.000 isolated
## 2239 3270.3488 0.000 isolated
## 2240 3270.3488 0.000 isolated
## 2241 3270.3488 0.000 isolated
## 2242 3270.3488 0.000 isolated
## 2243 3270.3488 0.000 isolated
## 2244 3270.3488 0.000 isolated
## 2245 3270.3488 0.000 isolated
## 2246 3270.3488 0.000 isolated
## 2247 3270.3488 0.000 isolated
## 2248 3270.3488 0.000 isolated
## 2249 3270.3488 0.000 isolated
## 2250 3270.3488 0.000 isolated
## 2251 3270.3488 0.000 isolated
## 2252 3270.3488 0.000 isolated
## 2253 3270.3488 0.000 isolated
## 2254 3270.3488 0.000 isolated
## 2255 3270.3488 0.000 isolated
## 2256 3270.3488 0.000 isolated
## 2257 218.0233 0.000 isolated
## 2258 218.0233 0.000 isolated
## 2259 218.0233 0.000 isolated
## 2260 218.0233 0.000 isolated
## 2261 218.0233 0.000 isolated
## 2262 218.0233 0.000 isolated
## 2263 218.0233 0.000 isolated
## 2264 218.0233 0.000 isolated
## 2265 218.0233 0.000 isolated
## 2266 218.0233 0.000 isolated
## 2267 218.0233 0.000 isolated
## 2268 218.0233 0.000 isolated
## 2269 218.0233 0.000 isolated
## 2270 218.0233 0.000 isolated
## 2271 218.0233 0.000 isolated
## 2272 218.0233 0.000 isolated
## 2273 218.0233 0.000 isolated
## 2274 218.0233 0.000 isolated
## 2275 218.0233 0.000 isolated
## 2276 218.0233 0.000 isolated
## 2277 218.0233 0.000 isolated
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## 2866 1077.0349 0.000 isolated
## 2867 1077.0349 0.000 isolated
## 2868 1077.0349 0.000 isolated
## 2869 1077.0349 0.000 isolated
## 2870 1077.0349 0.000 isolated
## 2871 1077.0349 0.000 isolated
## 2872 1077.0349 0.000 isolated
## 2873 1077.0349 0.000 isolated
## 2874 1077.0349 0.000 isolated
## 2875 1077.0349 0.000 isolated
## 2876 1077.0349 0.000 isolated
## 2877 1077.0349 0.000 isolated
## 2878 1077.0349 0.000 isolated
## 2879 1077.0349 0.000 isolated
## 2880 1077.0349 0.000 isolated
## 2881 981.9767 2243.895 isolated
## 2882 981.9767 2243.895 isolated
## 2883 981.9767 2243.895 isolated
## 2884 981.9767 2243.895 isolated
## 2885 981.9767 2243.895 isolated
## 2886 981.9767 2243.895 isolated
## 2887 981.9767 2243.895 isolated
## 2888 981.9767 2243.895 isolated
## 2889 981.9767 2243.895 isolated
## 2890 981.9767 2243.895 isolated
## 2891 981.9767 2243.895 isolated
## 2892 981.9767 2243.895 isolated
## 2893 981.9767 2243.895 isolated
## 2894 981.9767 2243.895 isolated
## 2895 981.9767 2243.895 isolated
## 2896 981.9767 2243.895 isolated
## 2897 981.9767 2243.895 isolated
## 2898 981.9767 2243.895 isolated
## 2899 981.9767 2243.895 isolated
## 2900 981.9767 2243.895 isolated
## 2901 981.9767 2243.895 isolated
## 2902 981.9767 2243.895 isolated
## 2903 981.9767 2243.895 isolated
## 2904 981.9767 2243.895 isolated
## 2905 1508.7209 0.000 isolated
## 2906 1508.7209 0.000 isolated
## 2907 1508.7209 0.000 isolated
## 2908 1508.7209 0.000 isolated
## 2909 1508.7209 0.000 isolated
## 2910 1508.7209 0.000 isolated
## 2911 1508.7209 0.000 isolated
## 2912 1508.7209 0.000 isolated
## 2913 1508.7209 0.000 isolated
## 2914 1508.7209 0.000 isolated
## 2915 1508.7209 0.000 isolated
## 2916 1508.7209 0.000 isolated
## 2917 1508.7209 0.000 isolated
## 2918 1508.7209 0.000 isolated
## 2919 1508.7209 0.000 isolated
## 2920 1508.7209 0.000 isolated
## 2921 1508.7209 0.000 isolated
## 2922 1508.7209 0.000 isolated
## 2923 1508.7209 0.000 isolated
## 2924 1508.7209 0.000 isolated
## 2925 1508.7209 0.000 isolated
## 2926 1508.7209 0.000 isolated
## 2927 1508.7209 0.000 isolated
## 2928 1508.7209 0.000 isolated
## 2929 1308.1395 0.000 isolated
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## 2951 1308.1395 0.000 isolated
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## 2953 2947.6744 0.000 isolated
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## 2956 2947.6744 0.000 isolated
## 2957 2947.6744 0.000 isolated
## 2958 2947.6744 0.000 isolated
## 2959 2947.6744 0.000 isolated
## 2960 2947.6744 0.000 isolated
## 2961 2947.6744 0.000 isolated
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## 2965 2947.6744 0.000 isolated
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## 2967 2947.6744 0.000 isolated
## 2968 2947.6744 0.000 isolated
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## 2974 2947.6744 0.000 isolated
## 2975 2947.6744 0.000 isolated
## 2976 2947.6744 0.000 isolated
## 2977 1090.1163 0.000 isolated
## 2978 1090.1163 0.000 isolated
## 2979 1090.1163 0.000 isolated
## 2980 1090.1163 0.000 isolated
## 2981 1090.1163 0.000 isolated
## 2982 1090.1163 0.000 isolated
## 2983 1090.1163 0.000 isolated
## 2984 1090.1163 0.000 isolated
## 2985 1090.1163 0.000 isolated
## 2986 1090.1163 0.000 isolated
## 2987 1090.1163 0.000 isolated
## 2988 1090.1163 0.000 isolated
## 2989 1090.1163 0.000 isolated
## 2990 1090.1163 0.000 isolated
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## 3008 767.4419 0.000 isolated
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## 3048 1351.7442 0.000 isolated
## 3049 187.5000 0.000 isolated
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## 3051 187.5000 0.000 isolated
## 3052 187.5000 0.000 isolated
## 3053 187.5000 0.000 isolated
## 3054 187.5000 0.000 isolated
## 3055 187.5000 0.000 isolated
## 3056 187.5000 0.000 isolated
## 3057 187.5000 0.000 isolated
## 3058 187.5000 0.000 isolated
## 3059 187.5000 0.000 isolated
## 3060 187.5000 0.000 isolated
## 3061 187.5000 0.000 isolated
## 3062 187.5000 0.000 isolated
## 3063 187.5000 0.000 isolated
## 3064 187.5000 0.000 isolated
## 3065 187.5000 0.000 isolated
## 3066 187.5000 0.000 isolated
## 3067 187.5000 0.000 isolated
## 3068 187.5000 0.000 isolated
## 3069 187.5000 0.000 isolated
## 3070 187.5000 0.000 isolated
## 3071 187.5000 0.000 isolated
## 3072 187.5000 0.000 isolated
## 3073 981.9767 2243.895 isolated
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## 3075 981.9767 2243.895 isolated
## 3076 981.9767 2243.895 isolated
## 3077 981.9767 2243.895 isolated
## 3078 981.9767 2243.895 isolated
## 3079 981.9767 2243.895 isolated
## 3080 981.9767 2243.895 isolated
## 3081 981.9767 2243.895 isolated
## 3082 981.9767 2243.895 isolated
## 3083 981.9767 2243.895 isolated
## 3084 981.9767 2243.895 isolated
## 3085 981.9767 2243.895 isolated
## 3086 981.9767 2243.895 isolated
## 3087 981.9767 2243.895 isolated
## 3088 981.9767 2243.895 isolated
## 3089 981.9767 2243.895 isolated
## 3090 981.9767 2243.895 isolated
## 3091 981.9767 2243.895 isolated
## 3092 981.9767 2243.895 isolated
## 3093 981.9767 2243.895 isolated
## 3094 981.9767 2243.895 isolated
## 3095 981.9767 2243.895 isolated
## 3096 981.9767 2243.895 isolated
## 3097 418.6047 0.000 isolated
## 3098 418.6047 0.000 isolated
## 3099 418.6047 0.000 isolated
## 3100 418.6047 0.000 isolated
## 3101 418.6047 0.000 isolated
## 3102 418.6047 0.000 isolated
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## 3118 418.6047 0.000 isolated
## 3119 418.6047 0.000 isolated
## 3120 418.6047 0.000 isolated
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## 3137 1220.9302 0.000 isolated
## 3138 1220.9302 0.000 isolated
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## 3151 3122.0930 0.000 isolated
## 3152 3122.0930 0.000 isolated
## 3153 3122.0930 0.000 isolated
## 3154 3122.0930 0.000 isolated
## 3155 3122.0930 0.000 isolated
## 3156 3122.0930 0.000 isolated
## 3157 3122.0930 0.000 isolated
## 3158 3122.0930 0.000 isolated
## 3159 3122.0930 0.000 isolated
## 3160 3122.0930 0.000 isolated
## 3161 3122.0930 0.000 isolated
## 3162 3122.0930 0.000 isolated
## 3163 3122.0930 0.000 isolated
## 3164 3122.0930 0.000 isolated
## 3165 3122.0930 0.000 isolated
## 3166 3122.0930 0.000 isolated
## 3167 3122.0930 0.000 isolated
## 3168 3122.0930 0.000 isolated
## 3169 0.0000 0.000 isolated
## 3170 0.0000 0.000 isolated
## 3171 0.0000 0.000 isolated
## 3172 0.0000 0.000 isolated
## 3173 0.0000 0.000 isolated
## 3174 0.0000 0.000 isolated
## 3175 0.0000 0.000 isolated
## 3176 0.0000 0.000 isolated
## 3177 0.0000 0.000 isolated
## 3178 0.0000 0.000 isolated
## 3179 0.0000 0.000 isolated
## 3180 0.0000 0.000 isolated
## 3181 0.0000 0.000 isolated
## 3182 0.0000 0.000 isolated
## 3183 0.0000 0.000 isolated
## 3184 0.0000 0.000 isolated
## 3185 0.0000 0.000 isolated
## 3186 0.0000 0.000 isolated
## 3187 0.0000 0.000 isolated
## 3188 0.0000 0.000 isolated
## 3189 0.0000 0.000 isolated
## 3190 0.0000 0.000 isolated
## 3191 0.0000 0.000 isolated
## 3192 0.0000 0.000 isolated
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## 3194 3270.3488 0.000 isolated
## 3195 3270.3488 0.000 isolated
## 3196 3270.3488 0.000 isolated
## 3197 3270.3488 0.000 isolated
## 3198 3270.3488 0.000 isolated
## 3199 3270.3488 0.000 isolated
## 3200 3270.3488 0.000 isolated
## 3201 3270.3488 0.000 isolated
## 3202 3270.3488 0.000 isolated
## 3203 3270.3488 0.000 isolated
## 3204 3270.3488 0.000 isolated
## 3205 3270.3488 0.000 isolated
## 3206 3270.3488 0.000 isolated
## 3207 3270.3488 0.000 isolated
## 3208 3270.3488 0.000 isolated
## 3209 3270.3488 0.000 isolated
## 3210 3270.3488 0.000 isolated
## 3211 3270.3488 0.000 isolated
## 3212 3270.3488 0.000 isolated
## 3213 3270.3488 0.000 isolated
## 3214 3270.3488 0.000 isolated
## 3215 3270.3488 0.000 isolated
## 3216 3270.3488 0.000 isolated
## 3217 109.0116 0.000 isolated
## 3218 109.0116 0.000 isolated
## 3219 109.0116 0.000 isolated
## 3220 109.0116 0.000 isolated
## 3221 109.0116 0.000 isolated
## 3222 109.0116 0.000 isolated
## 3223 109.0116 0.000 isolated
## 3224 109.0116 0.000 isolated
## 3225 109.0116 0.000 isolated
## patch_size_symmetry set ID_first_patch ID_second_patch
## 1 asymmetric 1 1 11
## 2 asymmetric 2 1 11
## 3 asymmetric 3 1 11
## 4 asymmetric 4 1 11
## 5 asymmetric 7 1 11
## 6 asymmetric 8 1 11
## 7 asymmetric 9 1 11
## 8 asymmetric 10 1 11
## 9 asymmetric 11 1 11
## 10 asymmetric 12 1 11
## 11 asymmetric 13 1 11
## 12 asymmetric 14 1 11
## 13 asymmetric 27 1 11
## 14 asymmetric 28 1 11
## 15 asymmetric 29 1 11
## 16 asymmetric 30 1 11
## 17 asymmetric 31 1 11
## 18 asymmetric 32 1 11
## 19 asymmetric 33 1 11
## 20 asymmetric 34 1 11
## 21 asymmetric 37 1 11
## 22 asymmetric 38 1 11
## 23 asymmetric 39 1 11
## 24 asymmetric 40 1 11
## 25 asymmetric 1 1 11
## 26 asymmetric 2 1 11
## 27 asymmetric 3 1 11
## 28 asymmetric 4 1 11
## 29 asymmetric 7 1 11
## 30 asymmetric 8 1 11
## 31 asymmetric 9 1 11
## 32 asymmetric 10 1 11
## 33 asymmetric 11 1 11
## 34 asymmetric 12 1 11
## 35 asymmetric 13 1 11
## 36 asymmetric 14 1 11
## 37 asymmetric 27 1 11
## 38 asymmetric 28 1 11
## 39 asymmetric 29 1 11
## 40 asymmetric 30 1 11
## 41 asymmetric 31 1 11
## 42 asymmetric 32 1 11
## 43 asymmetric 33 1 11
## 44 asymmetric 34 1 11
## 45 asymmetric 37 1 11
## 46 asymmetric 38 1 11
## 47 asymmetric 39 1 11
## 48 asymmetric 40 1 11
## 49 asymmetric 1 1 11
## 50 asymmetric 2 1 11
## 51 asymmetric 3 1 11
## 52 asymmetric 4 1 11
## 53 asymmetric 7 1 11
## 54 asymmetric 8 1 11
## 55 asymmetric 9 1 11
## 56 asymmetric 10 1 11
## 57 asymmetric 11 1 11
## 58 asymmetric 12 1 11
## 59 asymmetric 13 1 11
## 60 asymmetric 14 1 11
## 61 asymmetric 27 1 11
## 62 asymmetric 28 1 11
## 63 asymmetric 29 1 11
## 64 asymmetric 30 1 11
## 65 asymmetric 31 1 11
## 66 asymmetric 32 1 11
## 67 asymmetric 33 1 11
## 68 asymmetric 34 1 11
## 69 asymmetric 37 1 11
## 70 asymmetric 38 1 11
## 71 asymmetric 39 1 11
## 72 asymmetric 40 1 11
## 73 asymmetric 1 1 11
## 74 asymmetric 2 1 11
## 75 asymmetric 3 1 11
## 76 asymmetric 4 1 11
## 77 asymmetric 7 1 11
## 78 asymmetric 8 1 11
## 79 asymmetric 9 1 11
## 80 asymmetric 10 1 11
## 81 asymmetric 11 1 11
## 82 asymmetric 12 1 11
## 83 asymmetric 13 1 11
## 84 asymmetric 14 1 11
## 85 asymmetric 27 1 11
## 86 asymmetric 28 1 11
## 87 asymmetric 29 1 11
## 88 asymmetric 30 1 11
## 89 asymmetric 31 1 11
## 90 asymmetric 32 1 11
## 91 asymmetric 33 1 11
## 92 asymmetric 34 1 11
## 93 asymmetric 37 1 11
## 94 asymmetric 38 1 11
## 95 asymmetric 39 1 11
## 96 asymmetric 40 1 11
## 97 asymmetric 1 1 11
## 98 asymmetric 2 1 11
## 99 asymmetric 3 1 11
## 100 asymmetric 4 1 11
## 101 asymmetric 7 1 11
## 102 asymmetric 8 1 11
## 103 asymmetric 9 1 11
## 104 asymmetric 10 1 11
## 105 asymmetric 11 1 11
## 106 asymmetric 12 1 11
## 107 asymmetric 13 1 11
## 108 asymmetric 14 1 11
## 109 asymmetric 27 1 11
## 110 asymmetric 28 1 11
## 111 asymmetric 29 1 11
## 112 asymmetric 30 1 11
## 113 asymmetric 31 1 11
## 114 asymmetric 32 1 11
## 115 asymmetric 33 1 11
## 116 asymmetric 34 1 11
## 117 asymmetric 37 1 11
## 118 asymmetric 38 1 11
## 119 asymmetric 39 1 11
## 120 asymmetric 40 1 11
## 121 asymmetric 1 1 11
## 122 asymmetric 2 1 11
## 123 asymmetric 3 1 11
## 124 asymmetric 4 1 11
## 125 asymmetric 7 1 11
## 126 asymmetric 8 1 11
## 127 asymmetric 9 1 11
## 128 asymmetric 10 1 11
## 129 asymmetric 11 1 11
## 130 asymmetric 12 1 11
## 131 asymmetric 13 1 11
## 132 asymmetric 14 1 11
## 133 asymmetric 27 1 11
## 134 asymmetric 28 1 11
## 135 asymmetric 29 1 11
## 136 asymmetric 30 1 11
## 137 asymmetric 31 1 11
## 138 asymmetric 32 1 11
## 139 asymmetric 33 1 11
## 140 asymmetric 34 1 11
## 141 asymmetric 37 1 11
## 142 asymmetric 38 1 11
## 143 asymmetric 39 1 11
## 144 asymmetric 40 1 11
## 145 asymmetric 1 1 11
## 146 asymmetric 2 1 11
## 147 asymmetric 3 1 11
## 148 asymmetric 4 1 11
## 149 asymmetric 7 1 11
## 150 asymmetric 8 1 11
## 151 asymmetric 9 1 11
## 152 asymmetric 10 1 11
## 153 asymmetric 11 1 11
## 154 asymmetric 12 1 11
## 155 asymmetric 13 1 11
## 156 asymmetric 14 1 11
## 157 asymmetric 27 1 11
## 158 asymmetric 28 1 11
## 159 asymmetric 29 1 11
## 160 asymmetric 30 1 11
## 161 asymmetric 31 1 11
## 162 asymmetric 32 1 11
## 163 asymmetric 33 1 11
## 164 asymmetric 34 1 11
## 165 asymmetric 37 1 11
## 166 asymmetric 38 1 11
## 167 asymmetric 39 1 11
## 168 asymmetric 40 1 11
## 169 asymmetric 1 1 11
## 170 asymmetric 2 1 11
## 171 asymmetric 3 1 11
## 172 asymmetric 4 1 11
## 173 asymmetric 7 1 11
## 174 asymmetric 8 1 11
## 175 asymmetric 9 1 11
## 176 asymmetric 10 1 11
## 177 asymmetric 11 1 11
## 178 asymmetric 12 1 11
## 179 asymmetric 13 1 11
## 180 asymmetric 14 1 11
## 181 asymmetric 27 1 11
## 182 asymmetric 28 1 11
## 183 asymmetric 29 1 11
## 184 asymmetric 30 1 11
## 185 asymmetric 31 1 11
## 186 asymmetric 32 1 11
## 187 asymmetric 33 1 11
## 188 asymmetric 34 1 11
## 189 asymmetric 37 1 11
## 190 asymmetric 38 1 11
## 191 asymmetric 39 1 11
## 192 asymmetric 40 1 11
## 193 asymmetric 81 1 12
## 194 asymmetric 82 1 12
## 195 asymmetric 83 1 12
## 196 asymmetric 84 1 12
## 197 asymmetric 87 1 12
## 198 asymmetric 88 1 12
## 199 asymmetric 89 1 12
## 200 asymmetric 90 1 12
## 201 asymmetric 91 1 12
## 202 asymmetric 92 1 12
## 203 asymmetric 93 1 12
## 204 asymmetric 94 1 12
## 205 asymmetric 107 1 12
## 206 asymmetric 108 1 12
## 207 asymmetric 109 1 12
## 208 asymmetric 110 1 12
## 209 asymmetric 111 1 12
## 210 asymmetric 112 1 12
## 211 asymmetric 113 1 12
## 212 asymmetric 114 1 12
## 213 asymmetric 117 1 12
## 214 asymmetric 118 1 12
## 215 asymmetric 119 1 12
## 216 asymmetric 120 1 12
## 217 asymmetric 81 1 12
## 218 asymmetric 82 1 12
## 219 asymmetric 83 1 12
## 220 asymmetric 84 1 12
## 221 asymmetric 87 1 12
## 222 asymmetric 88 1 12
## 223 asymmetric 89 1 12
## 224 asymmetric 90 1 12
## 225 asymmetric 91 1 12
## 226 asymmetric 92 1 12
## 227 asymmetric 93 1 12
## 228 asymmetric 94 1 12
## 229 asymmetric 107 1 12
## 230 asymmetric 108 1 12
## 231 asymmetric 109 1 12
## 232 asymmetric 110 1 12
## 233 asymmetric 111 1 12
## 234 asymmetric 112 1 12
## 235 asymmetric 113 1 12
## 236 asymmetric 114 1 12
## 237 asymmetric 117 1 12
## 238 asymmetric 118 1 12
## 239 asymmetric 119 1 12
## 240 asymmetric 120 1 12
## 241 asymmetric 81 1 12
## 242 asymmetric 82 1 12
## 243 asymmetric 83 1 12
## 244 asymmetric 84 1 12
## 245 asymmetric 87 1 12
## 246 asymmetric 88 1 12
## 247 asymmetric 89 1 12
## 248 asymmetric 90 1 12
## 249 asymmetric 91 1 12
## 250 asymmetric 92 1 12
## 251 asymmetric 93 1 12
## 252 asymmetric 94 1 12
## 253 asymmetric 107 1 12
## 254 asymmetric 108 1 12
## 255 asymmetric 109 1 12
## 256 asymmetric 110 1 12
## 257 asymmetric 111 1 12
## 258 asymmetric 112 1 12
## 259 asymmetric 113 1 12
## 260 asymmetric 114 1 12
## 261 asymmetric 117 1 12
## 262 asymmetric 118 1 12
## 263 asymmetric 119 1 12
## 264 asymmetric 120 1 12
## 265 asymmetric 81 1 12
## 266 asymmetric 82 1 12
## 267 asymmetric 83 1 12
## 268 asymmetric 84 1 12
## 269 asymmetric 87 1 12
## 270 asymmetric 88 1 12
## 271 asymmetric 89 1 12
## 272 asymmetric 90 1 12
## 273 asymmetric 91 1 12
## 274 asymmetric 92 1 12
## 275 asymmetric 93 1 12
## 276 asymmetric 94 1 12
## 277 asymmetric 107 1 12
## 278 asymmetric 108 1 12
## 279 asymmetric 109 1 12
## 280 asymmetric 110 1 12
## 281 asymmetric 111 1 12
## 282 asymmetric 112 1 12
## 283 asymmetric 113 1 12
## 284 asymmetric 114 1 12
## 285 asymmetric 117 1 12
## 286 asymmetric 118 1 12
## 287 asymmetric 119 1 12
## 288 asymmetric 120 1 12
## 289 asymmetric 81 1 12
## 290 asymmetric 82 1 12
## 291 asymmetric 83 1 12
## 292 asymmetric 84 1 12
## 293 asymmetric 87 1 12
## 294 asymmetric 88 1 12
## 295 asymmetric 89 1 12
## 296 asymmetric 90 1 12
## 297 asymmetric 91 1 12
## 298 asymmetric 92 1 12
## 299 asymmetric 93 1 12
## 300 asymmetric 94 1 12
## 301 asymmetric 107 1 12
## 302 asymmetric 108 1 12
## 303 asymmetric 109 1 12
## 304 asymmetric 110 1 12
## 305 asymmetric 111 1 12
## 306 asymmetric 112 1 12
## 307 asymmetric 113 1 12
## 308 asymmetric 114 1 12
## 309 asymmetric 117 1 12
## 310 asymmetric 118 1 12
## 311 asymmetric 119 1 12
## 312 asymmetric 120 1 12
## 313 asymmetric 81 1 12
## 314 asymmetric 82 1 12
## 315 asymmetric 83 1 12
## 316 asymmetric 84 1 12
## 317 asymmetric 87 1 12
## 318 asymmetric 88 1 12
## 319 asymmetric 89 1 12
## 320 asymmetric 90 1 12
## 321 asymmetric 91 1 12
## 322 asymmetric 92 1 12
## 323 asymmetric 93 1 12
## 324 asymmetric 94 1 12
## 325 asymmetric 107 1 12
## 326 asymmetric 108 1 12
## 327 asymmetric 109 1 12
## 328 asymmetric 110 1 12
## 329 asymmetric 111 1 12
## 330 asymmetric 112 1 12
## 331 asymmetric 113 1 12
## 332 asymmetric 114 1 12
## 333 asymmetric 117 1 12
## 334 asymmetric 118 1 12
## 335 asymmetric 119 1 12
## 336 asymmetric 120 1 12
## 337 asymmetric 81 1 12
## 338 asymmetric 82 1 12
## 339 asymmetric 83 1 12
## 340 asymmetric 84 1 12
## 341 asymmetric 87 1 12
## 342 asymmetric 88 1 12
## 343 asymmetric 89 1 12
## 344 asymmetric 90 1 12
## 345 asymmetric 91 1 12
## 346 asymmetric 92 1 12
## 347 asymmetric 93 1 12
## 348 asymmetric 94 1 12
## 349 asymmetric 107 1 12
## 350 asymmetric 108 1 12
## 351 asymmetric 109 1 12
## 352 asymmetric 110 1 12
## 353 asymmetric 111 1 12
## 354 asymmetric 112 1 12
## 355 asymmetric 113 1 12
## 356 asymmetric 114 1 12
## 357 asymmetric 117 1 12
## 358 asymmetric 118 1 12
## 359 asymmetric 119 1 12
## 360 asymmetric 120 1 12
## 361 asymmetric 81 1 12
## 362 asymmetric 82 1 12
## 363 asymmetric 83 1 12
## 364 asymmetric 84 1 12
## 365 asymmetric 87 1 12
## 366 asymmetric 88 1 12
## 367 asymmetric 89 1 12
## 368 asymmetric 90 1 12
## 369 asymmetric 91 1 12
## 370 asymmetric 92 1 12
## 371 asymmetric 93 1 12
## 372 asymmetric 94 1 12
## 373 asymmetric 107 1 12
## 374 asymmetric 108 1 12
## 375 asymmetric 109 1 12
## 376 asymmetric 110 1 12
## 377 asymmetric 111 1 12
## 378 asymmetric 112 1 12
## 379 asymmetric 113 1 12
## 380 asymmetric 114 1 12
## 381 asymmetric 117 1 12
## 382 asymmetric 118 1 12
## 383 asymmetric 119 1 12
## 384 asymmetric 120 1 12
## 385 asymmetric 41 1 13
## 386 asymmetric 42 1 13
## 387 asymmetric 43 1 13
## 388 asymmetric 44 1 13
## 389 asymmetric 47 1 13
## 390 asymmetric 48 1 13
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## 3160 asymmetric 50 4 12
## 3161 asymmetric 51 4 12
## 3162 asymmetric 60 4 12
## 3163 asymmetric 63 4 12
## 3164 asymmetric 64 4 12
## 3165 asymmetric 65 4 12
## 3166 asymmetric 66 4 12
## 3167 asymmetric 67 4 12
## 3168 asymmetric 68 4 12
## 3169 asymmetric 13 4 12
## 3170 asymmetric 14 4 12
## 3171 asymmetric 15 4 12
## 3172 asymmetric 16 4 12
## 3173 asymmetric 17 4 12
## 3174 asymmetric 18 4 12
## 3175 asymmetric 21 4 12
## 3176 asymmetric 30 4 12
## 3177 asymmetric 31 4 12
## 3178 asymmetric 36 4 12
## 3179 asymmetric 37 4 12
## 3180 asymmetric 38 4 12
## 3181 asymmetric 43 4 12
## 3182 asymmetric 44 4 12
## 3183 asymmetric 45 4 12
## 3184 asymmetric 50 4 12
## 3185 asymmetric 51 4 12
## 3186 asymmetric 60 4 12
## 3187 asymmetric 63 4 12
## 3188 asymmetric 64 4 12
## 3189 asymmetric 65 4 12
## 3190 asymmetric 66 4 12
## 3191 asymmetric 67 4 12
## 3192 asymmetric 68 4 12
## 3193 asymmetric 13 4 12
## 3194 asymmetric 14 4 12
## 3195 asymmetric 15 4 12
## 3196 asymmetric 16 4 12
## 3197 asymmetric 17 4 12
## 3198 asymmetric 18 4 12
## 3199 asymmetric 21 4 12
## 3200 asymmetric 30 4 12
## 3201 asymmetric 31 4 12
## 3202 asymmetric 36 4 12
## 3203 asymmetric 37 4 12
## 3204 asymmetric 38 4 12
## 3205 asymmetric 43 4 12
## 3206 asymmetric 44 4 12
## 3207 asymmetric 45 4 12
## 3208 asymmetric 50 4 12
## 3209 asymmetric 51 4 12
## 3210 asymmetric 60 4 12
## 3211 asymmetric 63 4 12
## 3212 asymmetric 64 4 12
## 3213 asymmetric 65 4 12
## 3214 asymmetric 66 4 12
## 3215 asymmetric 67 4 12
## 3216 asymmetric 68 4 12
## 3217 asymmetric 13 4 12
## 3218 asymmetric 14 4 12
## 3219 asymmetric 15 4 12
## 3220 asymmetric 16 4 12
## 3221 asymmetric 17 4 12
## 3222 asymmetric 18 4 12
## 3223 asymmetric 21 4 12
## 3224 asymmetric 30 4 12
## 3225 asymmetric 31 4 12
## [ reached 'max' / getOption("max.print") -- omitted 6695 rows ]Filter according to disturbance
Here I’m filtering patches to have only the ones with disturbance low.
#Filter data sets according to the global disturbance
ds_individuals = ds_individuals %>%
filter(disturbance == disturbance_global_input)
ds_patches = ds_patches %>%
filter(disturbance == disturbance_global_input)
ds_patches_effect_size = ds_patches_effect_size %>%
filter(disturbance == disturbance_global_input)
ds_metaecosystems = ds_metaecosystems %>%
filter(disturbance == disturbance_global_input)ds_classes = ds_classes %>%
filter(disturbance == disturbance_global_input)
ds_classes_effect_size = ds_classes_effect_size %>%
filter(disturbance == disturbance_global_input)Plots & Analysis
All Meta-ecosystems
metaecosystem_type_i = c("Small-Small meta-ecosystem",
"Medium-Medium meta-ecosystem",
"Medium-Medium isolated",
"Large-Large meta-ecosystem",
"Small-Large meta-ecosystem",
"Small-Large isolated")Biomass
response_variable = "total_metaecosystem_bioarea_mm2"plot.metaecos.points(ds_metaecosystems,
metaecosystem_type_i,
response_variable,
3)
Boxplots
plot.metaecos.boxplots(ds_metaecosystems,
metaecosystem_type_i,
response_variable)
Replicates
plot.metaecos.replicates(ds_metaecosystems,
metaecosystem_type_i,
response_variable)
Alpha (mean Shannon)
response_variable = "mean_shannon"plot.metaecos.points(ds_metaecosystems,
metaecosystem_type_i,
response_variable,
3)
Boxplots
plot.metaecos.boxplots(ds_metaecosystems,
metaecosystem_type_i,
response_variable)
Replicates
plot.metaecos.replicates(ds_metaecosystems,
metaecosystem_type_i,
response_variable)
Beta (Bray-Curtis)
response_variable = "bray_curtis"plot.metaecos.points(ds_metaecosystems,
metaecosystem_type_i,
response_variable,
3)
Boxplots
plot.metaecos.boxplots(ds_metaecosystems,
metaecosystem_type_i,
response_variable)
Replicates
plot.metaecos.replicates(ds_metaecosystems,
metaecosystem_type_i,
response_variable)
Gamma (regional richness)
response_variable = "metaecosystem_richness"plot.metaecos.points(ds_metaecosystems,
metaecosystem_type_i,
response_variable,
3)
Boxplots
plot.metaecos.boxplots(ds_metaecosystems,
metaecosystem_type_i,
response_variable)
Replicates
plot.metaecos.replicates(ds_metaecosystems,
metaecosystem_type_i,
response_variable)
MM vs SL
Biomass
response_variable = "total_metaecosystem_bioarea_mm2"metaecosystem_type_i = c("Medium-Medium meta-ecosystem",
"Medium-Medium isolated",
"Small-Large meta-ecosystem",
"Small-Large isolated")plot.metaecos.points(ds_metaecosystems,
metaecosystem_type_i,
response_variable,
3)
Boxplots
plot.metaecos.boxplots(ds_metaecosystems,
metaecosystem_type_i,
response_variable)
Replicates
plot.metaecos.replicates(ds_metaecosystems,
metaecosystem_type_i,
response_variable)
MM vs SL meta-ecosystems
metaecosystem_type_i = c("Medium-Medium meta-ecosystem",
"Small-Large meta-ecosystem")filtered_data = ds_metaecosystems %>%
filter(time_point >= first_time_point_model,
time_point <= last_time_point_model,
metaecosystem_type %in% metaecosystem_type_i)plot.metaecos.points(filtered_data,
metaecosystem_type_i,
response_variable)
null_model = lmer(
get(response_variable) ~
day +
(day | system_nr),
data = filtered_data,
REML = FALSE,
control = lmerControl(optimizer = "Nelder_Mead")
)patch_size_symmetry and
patch_size_symmetry : day)
full_model = lmer(
get(response_variable) ~
day +
patch_size_symmetry +
patch_size_symmetry : day +
(day | system_nr),
data = filtered_data,
REML = FALSE,
control = lmerControl(optimizer = "Nelder_Mead")
)Click for model output
print(summary(full_model), digits = 1)## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
## method [lmerModLmerTest]
## Formula:
## get(response_variable) ~ day + patch_size_symmetry + patch_size_symmetry:day +
## (day | system_nr)
## Data: filtered_data
## Control: lmerControl(optimizer = "Nelder_Mead")
##
## AIC BIC logLik deviance df.resid
## 612.0 628.8 -298.0 596.0 52
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.78 -0.53 0.01 0.55 2.56
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## system_nr (Intercept) 1e+02 10.5
## day 3e-02 0.2 -1.00
## Residual 1e+03 34.0
## Number of obs: 60, groups: system_nr, 10
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 233.9 18.1 24.3 12.9 2e-12 ***
## day -6.7 0.9 45.7 -7.4 3e-09 ***
## patch_size_symmetrysymmetric 37.6 25.6 24.3 1.5 0.2
## day:patch_size_symmetrysymmetric -1.1 1.3 45.7 -0.9 0.4
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) day ptch__
## day -0.920
## ptch_sz_sym -0.707 0.651
## dy:ptch_sz_ 0.651 -0.707 -0.920
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')#Create resid vs fitted
res_vs_fit = filtered_data %>%
mutate(predicted = fitted(full_model),
residuals = resid(full_model)) %>%
plot_ly(x = ~predicted,
y = ~residuals,
type = "scatter",
mode = "markers",
marker = list(size = 5, color = "#4C78A8"),
text = paste(" System nr: ", filtered_data$system_nr, "<br>",
"Day: ", filtered_data$day, "<br>",
"Meta-ecosystem: ", filtered_data$metaecosystem_type, "<br>",
"Tot Biomass: ", round(filtered_data$total_metaecosystem_bioarea_mm2, digits = 0), "<br>"),
hoverinfo = "text") %>%
plotly::layout(
xaxis = list(title = "Fitted Values"),
yaxis = list(title = "Residuals")
)Click for residual plots
res_vs_fitqqnorm(resid(full_model))
model_stats_full = compute.model.stats(full_model,
null_model,
"mixed_model")
model_stats_full %>%
mutate(deltaAIC = round(deltaAIC, digits = 1),
p_value = round(p_value, digits = 3),
R2 = NULL,
evidence = "",
evidence = ifelse(p_value > 0.1,
"none",
evidence),
evidence = ifelse(p_value < 0.1,
"* weak",
evidence),
evidence = ifelse(p_value < 0.05,
"** moderate",
evidence),
evidence = ifelse(p_value < 0.01,
"*** strong",
evidence),
evidence = ifelse(p_value < 0.001,
"**** very strong",
evidence),
p_value = ifelse(p_value < 0.001,
"< 0.001",
p_value)) %>%
print()## deltaAIC p_value evidence
## 1 0.9 0.207 none
patch_size_symmetry)
reduced_model = lmer(
get(response_variable) ~
day +
patch_size_symmetry +
(day | system_nr),
data = filtered_data,
REML = FALSE,
control = lmerControl(optimizer = "Nelder_Mead")
)Click for model output
print(summary(reduced_model), digits = 1)## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
## method [lmerModLmerTest]
## Formula: get(response_variable) ~ day + patch_size_symmetry + (day | system_nr)
## Data: filtered_data
## Control: lmerControl(optimizer = "Nelder_Mead")
##
## AIC BIC logLik deviance df.resid
## 610.8 625.4 -298.4 596.8 53
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.74 -0.56 0.03 0.58 2.37
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## system_nr (Intercept) 1e+02 10.9
## day 4e-02 0.2 -1.00
## Residual 1e+03 34.2
## Number of obs: 60, groups: system_nr, 10
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 244.2 13.9 23.9 18 3e-15 ***
## day -7.3 0.6 43.0 -11 2e-14 ***
## patch_size_symmetrysymmetric 17.0 10.0 10.3 2 0.1
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) day
## day -0.860
## ptch_sz_sym -0.361 0.000
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')#Create resid vs fitted
res_vs_fit = filtered_data %>%
mutate(predicted = fitted(reduced_model),
residuals = resid(reduced_model)) %>%
plot_ly(x = ~predicted,
y = ~residuals,
type = "scatter",
mode = "markers",
marker = list(size = 5, color = "#4C78A8"),
text = paste(" System nr: ", filtered_data$system_nr, "<br>",
"Day: ", filtered_data$day, "<br>",
"Meta-ecosystem: ", filtered_data$metaecosystem_type, "<br>",
"Tot Biomass: ", round(filtered_data$total_metaecosystem_bioarea_mm2, digits = 0), "<br>"),
hoverinfo = "text") %>%
plotly::layout(
xaxis = list(title = "Fitted Values"),
yaxis = list(title = "Residuals")
)Click for residual plots
res_vs_fitqqnorm(resid(reduced_model))
model_stats_fixed = compute.model.stats(reduced_model,
null_model,
"mixed_model")
model_stats_fixed %>%
mutate(deltaAIC = round(deltaAIC, digits = 1),
p_value = round(p_value, digits = 3),
R2 = NULL,
evidence = "",
evidence = ifelse(p_value > 0.1,
"none",
evidence),
evidence = ifelse(p_value < 0.1,
"* weak",
evidence),
evidence = ifelse(p_value < 0.05,
"** moderate",
evidence),
evidence = ifelse(p_value < 0.01,
"*** strong",
evidence),
evidence = ifelse(p_value < 0.001,
"**** very strong",
evidence),
p_value = ifelse(p_value < 0.001,
"< 0.001",
p_value)) %>%
print()## deltaAIC p_value evidence
## 1 -0.4 0.122 noneresults_table = fill.results.table(
results_table,
response_variable,
metaecosystem_type_i,
model_stats_full,
model_stats_fixed
)SL (meta-ecosystems vs isolated)
metaecosystem_type_i = c("Small-Large meta-ecosystem",
"Small-Large isolated")filtered_data = ds_metaecosystems %>%
filter(time_point >= first_time_point_model,
time_point <= last_time_point_model,
metaecosystem_type %in% metaecosystem_type_i)plot.metaecos.points(filtered_data,
metaecosystem_type_i,
response_variable)
The p-values and ΔAIC of the connection are computed for all possible combinations of the isolated patches, creating a p-value and ΔAIC distributions. We keep as p-value and ΔAIC of the comparison the mean of such distributions.
Click for bootstrapping ΔAIC and p-value
If you see “Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel = ## 1e-15, : unused control arguments ignored”, Ignore it. It just means that you didn’t pass the control argument to the Nelder_Mead optimiser, so it uses the default.
# Compute stats for all patch combinations
isolated_combinations_sets_filtered = isolated_combinations_sets %>%
filter(disturbance == disturbance_global_input,
metaecosystem_type %in% metaecosystem_type_i)
n_sets = isolated_combinations_sets_filtered %>%
pull(set) %>%
max()
iterated_results_table = data.frame(
Response = as.character(NA),
Levels = as.character(NA),
ΔAIC_full = NA,
p_full = NA,
ΔR2_full = NA,
ΔAIC_fix = NA,
p_fix = NA,
ΔR2_fix = NA,
combination_set = NA,
system_nr_isolated_systems = as.character(NA)
) %>%
slice(-1)
for (set_input in 1:n_sets) {
system_nr_isolated_systems_input = isolated_combinations_sets_filtered %>%
filter(
metaecosystem_type %in% metaecosystem_type_i,
connection == "isolated",
set == set_input
) %>%
pull(system_nr)
filtered_data = ds_metaecosystems %>%
filter(
time_point >= first_time_point_model,
time_point <= last_time_point_model,
metaecosystem_type %in% metaecosystem_type_i,
connection == "connected" |
(connection == "isolated" &
system_nr %in% system_nr_isolated_systems_input)
)
null_model = lmer(
get(response_variable) ~
day +
(day | system_nr),
data = filtered_data,
REML = FALSE,
control = lmerControl(optimizer = optimizer_input,
optCtrl = list(method = method_input))
)
full_model = lmer(
get(response_variable) ~
day +
connection +
connection:day +
(day | system_nr),
data = filtered_data,
REML = FALSE,
control = lmerControl(optimizer = optimizer_input,
optCtrl = list(method = method_input))
)
model_stats_full = compute.model.stats(full_model,
null_model,
"mixed_model")
reduced_model = lmer(
get(response_variable) ~
day +
connection +
(day | system_nr),
data = filtered_data,
REML = FALSE,
control = lmerControl(optimizer = optimizer_input,
optCtrl = list(method = method_input))
)
model_stats_fixed = compute.model.stats(reduced_model,
null_model,
"mixed_model")
iterated_results_table = fill.results.table(
iterated_results_table,
response_variable,
metaecosystem_type_i,
model_stats_full,
model_stats_fixed
)
iterated_results_table$set[nrow(iterated_results_table)] = set_input
iterated_results_table$system_nr_isolated_systems[nrow(iterated_results_table)] = paste(system_nr_isolated_systems_input, collapse = " ")
}## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -2.3e+01## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -2.8e+01## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -4.0e+01## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## unable to evaluate scaled gradient## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge: degenerate Hessian with 1 negative eigenvalues## Warning: Model failed to converge with 1 negative eigenvalue: -7.6e+00## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -2.1e+00## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -3.5e-04## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -1.6e+00## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## unable to evaluate scaled gradient## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge: degenerate Hessian with 1 negative eigenvalues## Warning: Model failed to converge with 1 negative eigenvalue: -1.2e+00## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -2.3e+00## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -3.1e+00## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -3.3e+00## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -6.4e+00## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## unable to evaluate scaled gradient## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge: degenerate Hessian with 1 negative eigenvalues## Warning: Model failed to converge with 1 negative eigenvalue: -7.0e-01## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -2.9e+00## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -2.2e+01## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -3.8e+00## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -4.9e+00## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -6.1e+00## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -5.7e+00## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -1.5e+01## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -9.0e+00## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -2.2e-01## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -6.6e+01## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -1.4e+00## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -7.5e+01## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -3.4e+00## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -7.8e+00## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -2.6e+00## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -1.8e+00## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -2.0e+00## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -2.1e+01## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -1.2e+00## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -3.7e-01## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -1.6e+01## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -1.7e+00## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -3.4e+00## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -9.5e+00## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -2.4e+00## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -2.2e+00## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -2.1e+00## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -4.5e+01## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## unable to evaluate scaled gradient## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge: degenerate Hessian with 1 negative eigenvalues## Warning: Model failed to converge with 1 negative eigenvalue: -3.6e+00## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -2.0e+00## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -2.8e+00## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -9.1e+00## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -1.9e+00## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -4.3e-01## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -2.3e+00## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -1.0e+00## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -8.8e-02## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -1.0e+01## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -1.7e+01## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -2.6e+00## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -2.8e+00## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -5.7e-01## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -1.6e+00## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -6.8e+01## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -6.2e+00## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -4.8e+00## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -2.1e-02## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -6.9e+01## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -3.2e+00## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -1.5e+01## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -3.4e+01## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -5.7e-01## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -1.9e+00## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -2.9e+00## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -2.0e+00## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -8.7e+00## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -1.5e+00## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -2.1e+00## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -4.0e+00## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -2.7e+00## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -1.8e+00## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -8.8e-02## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## unable to evaluate scaled gradient## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge: degenerate Hessian with 1 negative eigenvalues## Warning: Model failed to converge with 1 negative eigenvalue: -8.5e-02## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## unable to evaluate scaled gradient## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge: degenerate Hessian with 1 negative eigenvalues## Warning: Model failed to converge with 1 negative eigenvalue: -2.0e-01## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -2.0e+01## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -2.1e+00## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -7.1e+00## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## unable to evaluate scaled gradient## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge: degenerate Hessian with 1 negative eigenvalues## Warning: Model failed to converge with 1 negative eigenvalue: -4.2e+00## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -7.4e+01## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -3.3e+00## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -2.9e+01## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -3.4e+00## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -1.5e+01## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -1.9e+00## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -1.9e+00## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -5.5e+00## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -1.9e+00## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -2.5e+01## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -2.8e+00## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -1.2e+00## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -7.3e+00## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## unable to evaluate scaled gradient## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge: degenerate Hessian with 1 negative eigenvalues## Warning: Model failed to converge with 1 negative eigenvalue: -8.3e-01## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -3.0e+00## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -7.6e-01## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -2.6e+00## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -2.2e+00## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -2.8e+01## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -2.7e+00## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -1.8e+01## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -2.3e+00## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -3.3e+00## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## unable to evaluate scaled gradient## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge: degenerate Hessian with 1 negative eigenvalues## Warning: Model failed to converge with 1 negative eigenvalue: -1.0e+01## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -2.4e+00## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## unable to evaluate scaled gradient## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge: degenerate Hessian with 1 negative eigenvalues## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
connection and connection : day)
Click for ΔAIC and p-value distributions
hist(iterated_results_table$ΔAIC_full, main = "Distribution of ΔAIC of the full model.") 
hist(iterated_results_table$p_full, main = "Distribution of p-values of the full model.") 
model_stats_full = data.frame(
deltaAIC = mean(iterated_results_table$ΔAIC_full),
p_value = mean(iterated_results_table$p_full),
R2 = NA
)
model_stats_full %>%
mutate(deltaAIC = round(deltaAIC, digits = 1),
p_value = round(p_value, digits = 3),
R2 = NULL,
evidence = "",
evidence = ifelse(p_value > 0.1,
"none",
evidence),
evidence = ifelse(p_value < 0.1,
"* weak",
evidence),
evidence = ifelse(p_value < 0.05,
"** moderate",
evidence),
evidence = ifelse(p_value < 0.01,
"*** strong",
evidence),
evidence = ifelse(p_value < 0.001,
"**** very strong",
evidence),
p_value = ifelse(p_value < 0.001,
"< 0.001",
p_value)) %>%
print()## deltaAIC p_value evidence
## 1 -0.5 0.105 noneClick for which system nr produced which AIC in the full model
iterated_results_table %>%
select(system_nr_isolated_systems,
ΔAIC_full) %>%
arrange(ΔAIC_full)## system_nr_isolated_systems ΔAIC_full
## 1 1004 1010 1012 1016 1023 -0.92627989
## 2 1001 1010 1013 1019 1022 -0.92194721
## 3 1002 1010 1011 1019 1023 -0.89185553
## 4 1001 1010 1012 1019 1023 -0.88284718
## 5 1004 1010 1011 1017 1023 -0.87435072
## 6 1001 1010 1014 1017 1023 -0.84078618
## 7 1002 1010 1014 1016 1023 -0.84063425
## 8 1004 1010 1011 1018 1022 -0.82425333
## 9 1002 1008 1014 1016 1025 -0.81977787
## 10 1002 1008 1011 1019 1025 -0.81768418
## 11 1001 1008 1014 1017 1025 -0.81140017
## 12 1001 1008 1012 1019 1025 -0.80370441
## 13 1004 1010 1012 1018 1021 -0.79621450
## 14 1004 1008 1011 1017 1025 -0.79244497
## 15 1004 1008 1012 1016 1025 -0.78410015
## 16 1004 1007 1013 1016 1025 -0.77541657
## 17 1002 1010 1013 1019 1021 -0.76954554
## 18 1004 1010 1013 1017 1021 -0.76313103
## 19 1004 1010 1013 1016 1022 -0.74894995
## 20 1002 1006 1013 1019 1025 -0.74485229
## 21 1003 1010 1012 1019 1021 -0.73502495
## 22 1003 1010 1011 1019 1022 -0.72651017
## 23 1002 1010 1014 1018 1021 -0.69591543
## 24 1003 1010 1014 1017 1021 -0.69505526
## 25 1001 1010 1014 1018 1022 -0.68099063
## 26 1001 1007 1014 1020 1023 -0.68067240
## 27 1003 1010 1014 1016 1022 -0.67967352
## 28 1001 1007 1015 1019 1023 -0.67772504
## 29 1004 1006 1013 1017 1025 -0.66379182
## 30 1004 1006 1012 1018 1025 -0.64501046
## 31 1005 1007 1014 1016 1023 -0.64333629
## 32 1001 1007 1013 1019 1025 -0.64002588
## 33 1003 1006 1012 1019 1025 -0.63557902
## 34 1004 1007 1011 1018 1025 -0.63088882
## 35 1002 1008 1015 1019 1021 -0.62702321
## 36 1002 1006 1015 1019 1023 -0.62646971
## 37 1005 1007 1011 1019 1023 -0.62391197
## 38 1001 1008 1015 1019 1022 -0.62293486
## 39 1003 1007 1011 1019 1025 -0.61984431
## 40 1002 1006 1014 1018 1025 -0.60906854
## 41 1004 1008 1015 1016 1022 -0.60592069
## 42 1001 1010 1012 1018 1024 -0.60590850
## 43 1003 1006 1014 1017 1025 -0.60439690
## 44 1004 1008 1015 1017 1021 -0.59406116
## 45 1001 1007 1014 1018 1025 -0.59338163
## 46 1003 1007 1014 1016 1025 -0.58832473
## 47 1005 1008 1014 1016 1022 -0.58579153
## 48 1005 1008 1011 1019 1022 -0.58548095
## 49 1005 1006 1014 1017 1023 -0.58496490
## 50 1004 1006 1015 1017 1023 -0.58426010
## 51 1001 1009 1013 1017 1025 -0.58343834
## 52 1005 1007 1014 1018 1021 -0.58254171
## 53 1003 1010 1011 1017 1024 -0.57912974
## 54 1004 1007 1015 1016 1023 -0.57899168
## 55 1004 1008 1011 1020 1022 -0.57788109
## 56 1005 1008 1014 1017 1021 -0.57389144
## 57 1003 1010 1012 1016 1024 -0.56819806
## 58 1005 1008 1012 1019 1021 -0.56324710
## 59 1004 1008 1012 1020 1021 -0.56052857
## 60 1005 1007 1013 1019 1021 -0.55707196
## 61 1002 1009 1013 1016 1025 -0.55634299
## 62 1004 1007 1011 1020 1023 -0.55496756
## 63 1003 1007 1015 1019 1021 -0.54985950
## 64 1005 1006 1012 1019 1023 -0.54923920
## 65 1002 1006 1014 1020 1023 -0.53388522
## 66 1001 1009 1013 1020 1022 -0.51570048
## 67 1005 1006 1013 1019 1022 -0.51091045
## 68 1002 1009 1013 1020 1021 -0.51038212
## 69 1003 1006 1015 1019 1022 -0.50953169
## 70 1002 1010 1013 1016 1024 -0.49051453
## 71 1001 1010 1013 1017 1024 -0.49047582
## 72 1001 1009 1015 1017 1023 -0.48313254
## 73 1002 1009 1015 1016 1023 -0.48286001
## 74 1002 1010 1011 1018 1024 -0.47146211
## 75 1004 1006 1015 1018 1022 -0.46582286
## 76 1004 1007 1015 1018 1021 -0.46202535
## 77 1001 1008 1014 1020 1022 -0.46179867
## 78 1001 1009 1015 1018 1022 -0.46055774
## 79 1004 1006 1013 1020 1022 -0.45784165
## 80 1004 1007 1013 1020 1021 -0.45462893
## 81 1005 1009 1013 1016 1022 -0.45427478
## 82 1002 1008 1014 1020 1021 -0.45341845
## 83 1005 1009 1013 1017 1021 -0.45029358
## 84 1003 1007 1011 1020 1024 -0.44064689
## 85 1002 1008 1015 1016 1024 -0.43341035
## 86 1001 1008 1015 1017 1024 -0.42867661
## 87 1004 1006 1012 1020 1023 -0.40742238
## 88 1003 1007 1014 1020 1021 -0.40688031
## 89 1005 1006 1014 1018 1022 -0.40575107
## 90 1003 1006 1014 1020 1022 -0.40050484
## 91 1002 1006 1013 1020 1024 -0.39965899
## 92 1002 1006 1015 1018 1024 -0.37661727
## 93 1002 1008 1011 1020 1024 -0.36814997
## 94 1003 1006 1012 1020 1024 -0.36224010
## 95 1005 1006 1013 1017 1024 -0.35972735
## 96 1003 1006 1015 1017 1024 -0.34952617
## 97 1001 1008 1012 1020 1024 -0.34155563
## 98 1002 1009 1011 1018 1025 -0.33045400
## 99 1005 1008 1011 1017 1024 -0.32708879
## 100 1003 1009 1011 1017 1025 -0.32117455
## 101 1003 1009 1012 1016 1025 -0.31198903
## 102 1001 1009 1012 1018 1025 -0.30792392
## 103 1005 1008 1012 1016 1024 -0.30362170
## 104 1001 1009 1012 1020 1023 -0.26376699
## 105 1002 1009 1011 1020 1023 -0.26186607
## 106 1005 1009 1012 1016 1023 -0.25049849
## 107 1005 1009 1011 1017 1023 -0.25047788
## 108 1003 1007 1015 1016 1024 -0.22664344
## 109 1005 1007 1013 1016 1024 -0.21734352
## 110 1005 1006 1012 1018 1024 -0.21288844
## 111 1003 1009 1015 1017 1021 -0.19180395
## 112 1002 1009 1015 1018 1021 -0.17119465
## 113 1003 1009 1012 1020 1021 -0.16699839
## 114 1005 1009 1012 1018 1021 -0.16241706
## 115 1003 1009 1015 1016 1022 -0.15771439
## 116 1003 1009 1011 1020 1022 -0.15173440
## 117 1005 1007 1011 1018 1024 -0.14928012
## 118 1005 1009 1011 1018 1022 -0.14298440
## 119 1001 1007 1013 1020 1024 -0.10287060
## 120 1001 1007 1015 1018 1024 -0.07807924
connection)
Click for ΔAIC and p-value dsitributions
hist(iterated_results_table$ΔAIC_fix, main = "Distribution of ΔAIC of the fixed model.") 
hist(iterated_results_table$p_fix, main = "Distribution of p-values of the fixed model.") 
model_stats_fixed = data.frame(
deltaAIC = mean(iterated_results_table$ΔAIC_fix),
p_value = mean(iterated_results_table$p_fix),
R2 = NA
)
model_stats_fixed %>%
mutate(deltaAIC = round(deltaAIC, digits = 1),
p_value = round(p_value, digits = 3),
R2 = NULL,
evidence = "",
evidence = ifelse(p_value > 0.1,
"none",
evidence),
evidence = ifelse(p_value < 0.1,
"* weak",
evidence),
evidence = ifelse(p_value < 0.05,
"** moderate",
evidence),
evidence = ifelse(p_value < 0.01,
"*** strong",
evidence),
evidence = ifelse(p_value < 0.001,
"**** very strong",
evidence),
p_value = ifelse(p_value < 0.001,
"< 0.001",
p_value)) %>%
print()## deltaAIC p_value evidence
## 1 0.7 0.264 noneresults_table = fill.results.table(
results_table,
response_variable,
metaecosystem_type_i,
model_stats_full,
model_stats_fixed
)MM (meta-ecosystems vs isolated)
metaecosystem_type_i = c("Medium-Medium meta-ecosystem",
"Medium-Medium isolated")filtered_data = ds_metaecosystems %>%
filter(time_point >= first_time_point_model,
time_point <= last_time_point_model,
metaecosystem_type %in% metaecosystem_type_i)plot.metaecos.points(filtered_data,
metaecosystem_type_i,
response_variable)
The p-values and ΔAIC of the connection are computed for all possible combinations of the isolated patches, creating a p-value and ΔAIC distributions. We keep as p-value and ΔAIC of the comparison the mean of such distributions.
Click for bootstrapping ΔAIC and p-value
If you see “Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel = ## 1e-15, : unused control arguments ignored”, Ignore it. It just means that you didn’t pass the control argument to the Nelder_Mead optimiser, so it uses the default.
# Compute stats for all patch combinations
isolated_combinations_sets_filtered = isolated_combinations_sets %>%
filter(disturbance == disturbance_global_input,
metaecosystem_type %in% metaecosystem_type_i)
n_sets = isolated_combinations_sets_filtered %>%
pull(set) %>%
max()
iterated_results_table = data.frame(
Response = as.character(NA),
Levels = as.character(NA),
ΔAIC_full = NA,
p_full = NA,
ΔR2_full = NA,
ΔAIC_fix = NA,
p_fix = NA,
ΔR2_fix = NA,
combination_set = NA,
system_nr_isolated_systems = as.character(NA)
) %>%
slice(-1)
for (set_input in 1:n_sets) {
system_nr_isolated_systems_input = isolated_combinations_sets_filtered %>%
filter(
metaecosystem_type %in% metaecosystem_type_i,
connection == "isolated",
set == set_input
) %>%
pull(system_nr)
filtered_data = ds_metaecosystems %>%
filter(
time_point >= first_time_point_model,
time_point <= last_time_point_model,
metaecosystem_type %in% metaecosystem_type_i,
connection == "connected" |
(connection == "isolated" &
system_nr %in% system_nr_isolated_systems_input)
)
null_model = lmer(
get(response_variable) ~
day +
(day | system_nr),
data = filtered_data,
REML = FALSE,
control = lmerControl(optimizer = optimizer_input,
optCtrl = list(method = method_input))
)
full_model = lmer(
get(response_variable) ~
day +
connection +
connection:day +
(day | system_nr),
data = filtered_data,
REML = FALSE,
control = lmerControl(optimizer = optimizer_input,
optCtrl = list(method = method_input))
)
model_stats_full = compute.model.stats(full_model,
null_model,
"mixed_model")
reduced_model = lmer(
get(response_variable) ~
day +
connection +
(day | system_nr),
data = filtered_data,
REML = FALSE,
control = lmerControl(optimizer = optimizer_input,
optCtrl = list(method = method_input))
)
model_stats_fixed = compute.model.stats(reduced_model,
null_model,
"mixed_model")
iterated_results_table = fill.results.table(
iterated_results_table,
response_variable,
metaecosystem_type_i,
model_stats_full,
model_stats_fixed
)
iterated_results_table$set[nrow(iterated_results_table)] = set_input
iterated_results_table$system_nr_isolated_systems[nrow(iterated_results_table)] = paste(system_nr_isolated_systems_input, collapse = " ")
}## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
connection and connection : day)
Click for ΔAIC and p-value distributions
hist(iterated_results_table$ΔAIC_full, main = "Distribution of ΔAIC of the full model.") 
hist(iterated_results_table$p_full, main = "Distribution of p-values of the full model.") 
model_stats_full = data.frame(
deltaAIC = mean(iterated_results_table$ΔAIC_full),
p_value = mean(iterated_results_table$p_full),
R2 = NA
)
model_stats_full %>%
mutate(deltaAIC = round(deltaAIC, digits = 1),
p_value = round(p_value, digits = 3),
R2 = NULL,
evidence = "",
evidence = ifelse(p_value > 0.1,
"none",
evidence),
evidence = ifelse(p_value < 0.1,
"* weak",
evidence),
evidence = ifelse(p_value < 0.05,
"** moderate",
evidence),
evidence = ifelse(p_value < 0.01,
"*** strong",
evidence),
evidence = ifelse(p_value < 0.001,
"**** very strong",
evidence),
p_value = ifelse(p_value < 0.001,
"< 0.001",
p_value)) %>%
print()## deltaAIC p_value evidence
## 1 -1.2 0.078 * weakClick for which system nr produced which AIC in the full model
iterated_results_table %>%
select(system_nr_isolated_systems,
ΔAIC_full) %>%
arrange(ΔAIC_full)## system_nr_isolated_systems ΔAIC_full
## 1 1051 1054 -1.92651033
## 2 1054 1051 -1.92651033
## 3 1050 1055 -1.84623870
## 4 1055 1050 -1.84623870
## 5 1052 1053 -1.74248955
## 6 1053 1052 -1.74248955
## 7 1047 1051 -1.64874878
## 8 1051 1047 -1.64874878
## 9 1047 1055 -1.57909579
## 10 1055 1047 -1.57909579
## 11 1047 1052 -1.45439502
## 12 1052 1047 -1.45439502
## 13 1048 1050 -1.43231868
## 14 1050 1048 -1.43231868
## 15 1048 1054 -1.41715448
## 16 1054 1048 -1.41715448
## 17 1046 1053 -1.32907802
## 18 1053 1046 -1.32907802
## 19 1046 1054 -1.29569680
## 20 1054 1046 -1.29569680
## 21 1049 1053 -1.21633078
## 22 1053 1049 -1.21633078
## 23 1049 1050 -1.19859008
## 24 1050 1049 -1.19859008
## 25 1048 1052 -0.04237190
## 26 1052 1048 -0.04237190
## 27 1046 1055 -0.03078654
## 28 1055 1046 -0.03078654
## 29 1049 1051 0.02308620
## 30 1051 1049 0.02308620
connection)
Click for ΔAIC and p-value dsitributions
hist(iterated_results_table$ΔAIC_fix, main = "Distribution of ΔAIC of the fixed model.") 
hist(iterated_results_table$p_fix, main = "Distribution of p-values of the fixed model.") 
model_stats_fixed = data.frame(
deltaAIC = mean(iterated_results_table$ΔAIC_fix),
p_value = mean(iterated_results_table$p_fix),
R2 = NA
)
model_stats_fixed %>%
mutate(deltaAIC = round(deltaAIC, digits = 1),
p_value = round(p_value, digits = 3),
R2 = NULL,
evidence = "",
evidence = ifelse(p_value > 0.1,
"none",
evidence),
evidence = ifelse(p_value < 0.1,
"* weak",
evidence),
evidence = ifelse(p_value < 0.05,
"** moderate",
evidence),
evidence = ifelse(p_value < 0.01,
"*** strong",
evidence),
evidence = ifelse(p_value < 0.001,
"**** very strong",
evidence),
p_value = ifelse(p_value < 0.001,
"< 0.001",
p_value)) %>%
print()## deltaAIC p_value evidence
## 1 -3.1 0.026 ** moderateresults_table = fill.results.table(
results_table,
response_variable,
metaecosystem_type_i,
model_stats_full,
model_stats_fixed
)Alpha (mean Shannon)
response_variable = "mean_shannon"metaecosystem_type_i = c("Medium-Medium meta-ecosystem",
"Medium-Medium isolated",
"Small-Large meta-ecosystem",
"Small-Large isolated")plot.metaecos.points(ds_metaecosystems,
metaecosystem_type_i,
response_variable,
3)
Boxplots
plot.metaecos.boxplots(ds_metaecosystems,
metaecosystem_type_i,
response_variable)
Replicates
plot.metaecos.replicates(ds_metaecosystems,
metaecosystem_type_i,
response_variable)
MM vs SL meta-ecosystems
metaecosystem_type_i = c("Medium-Medium meta-ecosystem",
"Small-Large meta-ecosystem")filtered_data = ds_metaecosystems %>%
filter(time_point >= first_time_point_model,
time_point <= last_time_point_model,
metaecosystem_type %in% metaecosystem_type_i)plot.metaecos.points(filtered_data,
metaecosystem_type_i,
response_variable)
null_model = lmer(
get(response_variable) ~
day +
(day | system_nr),
data = filtered_data,
REML = FALSE,
control = lmerControl(optimizer = "Nelder_Mead")
)patch_size_symmetry and
patch_size_symmetry : day)
full_model = lmer(
get(response_variable) ~
day +
patch_size_symmetry +
patch_size_symmetry : day +
(day | system_nr),
data = filtered_data,
REML = FALSE,
control = lmerControl(optimizer = "Nelder_Mead")
)Click for model output
print(summary(full_model), digits = 1)## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
## method [lmerModLmerTest]
## Formula:
## get(response_variable) ~ day + patch_size_symmetry + patch_size_symmetry:day +
## (day | system_nr)
## Data: filtered_data
## Control: lmerControl(optimizer = "Nelder_Mead")
##
## AIC BIC logLik deviance df.resid
## -12.6 4.2 14.3 -28.6 52
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.4 -0.6 0.1 0.7 1.9
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## system_nr (Intercept) 9e-03 0.092
## day 3e-06 0.002 -1.00
## Residual 3e-02 0.183
## Number of obs: 60, groups: system_nr, 10
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.340 0.103 19.481 13.0 4e-11
## day 0.003 0.005 39.884 0.7 0.5
## patch_size_symmetrysymmetric 0.022 0.145 19.481 0.1 0.9
## day:patch_size_symmetrysymmetric -0.005 0.007 39.884 -0.7 0.5
##
## (Intercept) ***
## day
## patch_size_symmetrysymmetric
## day:patch_size_symmetrysymmetric
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) day ptch__
## day -0.910
## ptch_sz_sym -0.707 0.644
## dy:ptch_sz_ 0.644 -0.707 -0.910
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')#Create resid vs fitted
res_vs_fit = filtered_data %>%
mutate(predicted = fitted(full_model),
residuals = resid(full_model)) %>%
plot_ly(x = ~predicted,
y = ~residuals,
type = "scatter",
mode = "markers",
marker = list(size = 5, color = "#4C78A8"),
text = paste(" System nr: ", filtered_data$system_nr, "<br>",
"Day: ", filtered_data$day, "<br>",
"Meta-ecosystem: ", filtered_data$metaecosystem_type, "<br>",
"Tot Biomass: ", round(filtered_data$total_metaecosystem_bioarea_mm2, digits = 0), "<br>"),
hoverinfo = "text") %>%
plotly::layout(
xaxis = list(title = "Fitted Values"),
yaxis = list(title = "Residuals")
)Click for residual plots
res_vs_fitqqnorm(resid(full_model))
model_stats_full = compute.model.stats(full_model,
null_model,
"mixed_model")
model_stats_full %>%
mutate(deltaAIC = round(deltaAIC, digits = 1),
p_value = round(p_value, digits = 3),
R2 = NULL,
evidence = "",
evidence = ifelse(p_value > 0.1,
"none",
evidence),
evidence = ifelse(p_value < 0.1,
"* weak",
evidence),
evidence = ifelse(p_value < 0.05,
"** moderate",
evidence),
evidence = ifelse(p_value < 0.01,
"*** strong",
evidence),
evidence = ifelse(p_value < 0.001,
"**** very strong",
evidence),
p_value = ifelse(p_value < 0.001,
"< 0.001",
p_value)) %>%
print()## deltaAIC p_value evidence
## 1 2.5 0.464 none
patch_size_symmetry)
reduced_model = lmer(
get(response_variable) ~
day +
patch_size_symmetry +
(day | system_nr),
data = filtered_data,
REML = FALSE,
control = lmerControl(optimizer = "Nelder_Mead")
)Click for model output
print(summary(reduced_model), digits = 1)## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
## method [lmerModLmerTest]
## Formula: get(response_variable) ~ day + patch_size_symmetry + (day | system_nr)
## Data: filtered_data
## Control: lmerControl(optimizer = "Nelder_Mead")
##
## AIC BIC logLik deviance df.resid
## -14.2 0.5 14.1 -28.2 53
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.5 -0.6 0.1 0.7 1.9
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## system_nr (Intercept) 9e-03 0.094
## day 4e-06 0.002 -1.00
## Residual 3e-02 0.183
## Number of obs: 60, groups: system_nr, 10
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.383 0.079 21.073 17.5 5e-14 ***
## day 0.001 0.004 38.497 0.3 0.7
## patch_size_symmetrysymmetric -0.065 0.060 10.623 -1.1 0.3
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) day
## day -0.843
## ptch_sz_sym -0.381 0.000
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')#Create resid vs fitted
res_vs_fit = filtered_data %>%
mutate(predicted = fitted(reduced_model),
residuals = resid(reduced_model)) %>%
plot_ly(x = ~predicted,
y = ~residuals,
type = "scatter",
mode = "markers",
marker = list(size = 5, color = "#4C78A8"),
text = paste(" System nr: ", filtered_data$system_nr, "<br>",
"Day: ", filtered_data$day, "<br>",
"Meta-ecosystem: ", filtered_data$metaecosystem_type, "<br>",
"Tot Biomass: ", round(filtered_data$total_metaecosystem_bioarea_mm2, digits = 0), "<br>"),
hoverinfo = "text") %>%
plotly::layout(
xaxis = list(title = "Fitted Values"),
yaxis = list(title = "Residuals")
)Click for residual plots
res_vs_fitqqnorm(resid(reduced_model))
model_stats_fixed = compute.model.stats(reduced_model,
null_model,
"mixed_model")
model_stats_fixed %>%
mutate(deltaAIC = round(deltaAIC, digits = 1),
p_value = round(p_value, digits = 3),
R2 = NULL,
evidence = "",
evidence = ifelse(p_value > 0.1,
"none",
evidence),
evidence = ifelse(p_value < 0.1,
"* weak",
evidence),
evidence = ifelse(p_value < 0.05,
"** moderate",
evidence),
evidence = ifelse(p_value < 0.01,
"*** strong",
evidence),
evidence = ifelse(p_value < 0.001,
"**** very strong",
evidence),
p_value = ifelse(p_value < 0.001,
"< 0.001",
p_value)) %>%
print()## deltaAIC p_value evidence
## 1 0.9 0.293 noneresults_table = fill.results.table(
results_table,
response_variable,
metaecosystem_type_i,
model_stats_full,
model_stats_fixed
)SL (meta-ecosystems vs isolated)
metaecosystem_type_i = c("Small-Large meta-ecosystem",
"Small-Large isolated")filtered_data = ds_metaecosystems %>%
filter(time_point >= first_time_point_model,
time_point <= last_time_point_model,
metaecosystem_type %in% metaecosystem_type_i)plot.metaecos.points(filtered_data,
metaecosystem_type_i,
response_variable)
The p-values and ΔAIC of the connection are computed for all possible combinations of the isolated patches, creating a p-value and ΔAIC distributions. We keep as p-value and ΔAIC of the comparison the mean of such distributions.
Click for bootstrapping ΔAIC and p-value
If you see “Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel = ## 1e-15, : unused control arguments ignored”, Ignore it. It just means that you didn’t pass the control argument to the Nelder_Mead optimiser, so it uses the default.
# Compute stats for all patch combinations
isolated_combinations_sets_filtered = isolated_combinations_sets %>%
filter(disturbance == disturbance_global_input,
metaecosystem_type %in% metaecosystem_type_i)
n_sets = isolated_combinations_sets_filtered %>%
pull(set) %>%
max()
iterated_results_table = data.frame(
Response = as.character(NA),
Levels = as.character(NA),
ΔAIC_full = NA,
p_full = NA,
ΔR2_full = NA,
ΔAIC_fix = NA,
p_fix = NA,
ΔR2_fix = NA,
combination_set = NA,
system_nr_isolated_systems = as.character(NA)
) %>%
slice(-1)
for (set_input in 1:n_sets) {
system_nr_isolated_systems_input = isolated_combinations_sets_filtered %>%
filter(
metaecosystem_type %in% metaecosystem_type_i,
connection == "isolated",
set == set_input
) %>%
pull(system_nr)
filtered_data = ds_metaecosystems %>%
filter(
time_point >= first_time_point_model,
time_point <= last_time_point_model,
metaecosystem_type %in% metaecosystem_type_i,
connection == "connected" |
(connection == "isolated" &
system_nr %in% system_nr_isolated_systems_input)
)
null_model = lmer(
get(response_variable) ~
day +
(day | system_nr),
data = filtered_data,
REML = FALSE,
control = lmerControl(optimizer = optimizer_input,
optCtrl = list(method = method_input))
)
full_model = lmer(
get(response_variable) ~
day +
connection +
connection:day +
(day | system_nr),
data = filtered_data,
REML = FALSE,
control = lmerControl(optimizer = optimizer_input,
optCtrl = list(method = method_input))
)
model_stats_full = compute.model.stats(full_model,
null_model,
"mixed_model")
reduced_model = lmer(
get(response_variable) ~
day +
connection +
(day | system_nr),
data = filtered_data,
REML = FALSE,
control = lmerControl(optimizer = optimizer_input,
optCtrl = list(method = method_input))
)
model_stats_fixed = compute.model.stats(reduced_model,
null_model,
"mixed_model")
iterated_results_table = fill.results.table(
iterated_results_table,
response_variable,
metaecosystem_type_i,
model_stats_full,
model_stats_fixed
)
iterated_results_table$set[nrow(iterated_results_table)] = set_input
iterated_results_table$system_nr_isolated_systems[nrow(iterated_results_table)] = paste(system_nr_isolated_systems_input, collapse = " ")
}## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -6.6e+01## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -6.1e+02## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -1.5e+02## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -4.5e+00## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -4.6e-01## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -2.7e+03## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -1.4e+02## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -1.4e+03## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -6.8e-02## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -1.2e+03## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -1.5e+01## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -1.8e+03## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -4.4e+02## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -3.6e+02## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -1.7e+02## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -2.8e+03## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -5.2e+01## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -3.0e+03## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -1.4e+03## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -1.2e+02## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -1.5e+01## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -5.9e+01## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -3.9e+02## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -1.9e+02## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -3.1e+00## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -5.8e+03## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -3.3e+03## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -1.6e+00## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -3.6e+00## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -2.2e+02## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -4.4e+02## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -1.0e+02## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -4.9e+01## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -9.5e+02## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -1.7e+03## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -2.5e+02## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -1.1e+02## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -4.1e+02## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -8.0e+01## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -4.5e+02## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -2.7e+02## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -1.1e+02## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -1.4e+02## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -2.6e+02## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -3.1e+02
connection and connection : day)
Click for ΔAIC and p-value distributions
hist(iterated_results_table$ΔAIC_full, main = "Distribution of ΔAIC of the full model.") 
hist(iterated_results_table$p_full, main = "Distribution of p-values of the full model.") 
model_stats_full = data.frame(
deltaAIC = mean(iterated_results_table$ΔAIC_full),
p_value = mean(iterated_results_table$p_full),
R2 = NA
)
model_stats_full %>%
mutate(deltaAIC = round(deltaAIC, digits = 1),
p_value = round(p_value, digits = 3),
R2 = NULL,
evidence = "",
evidence = ifelse(p_value > 0.1,
"none",
evidence),
evidence = ifelse(p_value < 0.1,
"* weak",
evidence),
evidence = ifelse(p_value < 0.05,
"** moderate",
evidence),
evidence = ifelse(p_value < 0.01,
"*** strong",
evidence),
evidence = ifelse(p_value < 0.001,
"**** very strong",
evidence),
p_value = ifelse(p_value < 0.001,
"< 0.001",
p_value)) %>%
print()## deltaAIC p_value evidence
## 1 -9.4 0.002 *** strongClick for which system nr produced which AIC in the full model
iterated_results_table %>%
select(system_nr_isolated_systems,
ΔAIC_full) %>%
arrange(ΔAIC_full)## system_nr_isolated_systems ΔAIC_full
## 1 1004 1008 1015 1016 1022 -19.249457
## 2 1005 1007 1011 1019 1023 -17.020063
## 3 1005 1007 1013 1019 1021 -16.984911
## 4 1005 1006 1013 1017 1024 -15.299917
## 5 1001 1010 1014 1018 1022 -13.462412
## 6 1001 1007 1014 1018 1025 -13.461151
## 7 1003 1010 1014 1016 1022 -13.105110
## 8 1001 1009 1012 1018 1025 -13.030802
## 9 1003 1007 1014 1016 1025 -13.028598
## 10 1001 1010 1012 1018 1024 -12.983783
## 11 1004 1007 1011 1018 1025 -12.872192
## 12 1004 1010 1011 1018 1022 -12.792253
## 13 1003 1010 1012 1016 1024 -12.672256
## 14 1003 1009 1012 1016 1025 -12.520275
## 15 1002 1010 1011 1018 1024 -12.453324
## 16 1001 1009 1015 1018 1022 -12.332001
## 17 1005 1009 1011 1018 1022 -12.330034
## 18 1002 1009 1011 1018 1025 -12.283299
## 19 1004 1007 1013 1016 1025 -11.957878
## 20 1003 1009 1015 1016 1022 -11.918491
## 21 1004 1010 1013 1016 1022 -11.898049
## 22 1001 1007 1015 1018 1024 -11.850347
## 23 1005 1007 1011 1018 1024 -11.647281
## 24 1003 1007 1015 1016 1024 -11.568210
## 25 1002 1010 1013 1016 1024 -11.529644
## 26 1005 1009 1013 1016 1022 -11.348359
## 27 1003 1007 1011 1019 1025 -11.346753
## 28 1002 1009 1013 1016 1025 -11.341393
## 29 1003 1010 1011 1019 1022 -11.255111
## 30 1001 1007 1013 1019 1025 -11.003809
## 31 1001 1010 1013 1019 1022 -10.920166
## 32 1005 1007 1013 1016 1024 -10.854462
## 33 1004 1006 1012 1018 1025 -10.652597
## 34 1002 1010 1014 1018 1021 -10.519464
## 35 1004 1010 1012 1018 1021 -10.516555
## 36 1002 1006 1014 1018 1025 -10.447518
## 37 1003 1010 1011 1017 1024 -10.413239
## 38 1005 1009 1012 1018 1021 -10.351110
## 39 1003 1009 1011 1017 1025 -10.272903
## 40 1001 1010 1013 1017 1024 -10.042969
## 41 1005 1006 1014 1018 1022 -10.011042
## 42 1005 1006 1012 1018 1024 -10.008257
## 43 1002 1009 1015 1018 1021 -9.985341
## 44 1001 1009 1013 1017 1025 -9.981875
## 45 1005 1007 1014 1018 1021 -9.952512
## 46 1003 1009 1011 1020 1022 -9.892339
## 47 1004 1006 1015 1018 1022 -9.842749
## 48 1003 1006 1012 1019 1025 -9.807286
## 49 1004 1007 1015 1018 1021 -9.786107
## 50 1002 1006 1015 1018 1024 -9.708343
## 51 1003 1007 1011 1020 1024 -9.642038
## 52 1001 1009 1013 1020 1022 -9.469370
## 53 1004 1008 1012 1016 1025 -9.394285
## 54 1003 1010 1012 1019 1021 -9.329551
## 55 1001 1007 1013 1020 1024 -9.234592
## 56 1003 1006 1014 1017 1025 -9.153448
## 57 1001 1008 1012 1019 1025 -9.111903
## 58 1004 1010 1012 1016 1023 -9.082423
## 59 1002 1010 1014 1016 1023 -8.975994
## 60 1003 1010 1014 1017 1021 -8.856601
## 61 1003 1006 1015 1019 1022 -8.741857
## 62 1003 1007 1015 1019 1021 -8.593798
## 63 1005 1009 1012 1016 1023 -8.589662
## 64 1001 1008 1014 1017 1025 -8.497198
## 65 1004 1007 1015 1016 1023 -8.478950
## 66 1005 1007 1014 1016 1023 -8.456976
## 67 1005 1008 1014 1016 1022 -8.446301
## 68 1004 1006 1013 1017 1025 -8.440749
## 69 1002 1010 1013 1019 1021 -8.426875
## 70 1002 1008 1011 1019 1025 -8.411446
## 71 1005 1008 1012 1016 1024 -8.401542
## 72 1002 1009 1015 1016 1023 -8.384876
## 73 1002 1008 1015 1016 1024 -8.267571
## 74 1002 1008 1014 1016 1025 -8.251191
## 75 1004 1010 1013 1017 1021 -8.214667
## 76 1003 1009 1012 1020 1021 -8.203788
## 77 1003 1009 1015 1017 1021 -8.137319
## 78 1001 1008 1015 1019 1022 -8.114672
## 79 1003 1006 1015 1017 1024 -8.093688
## 80 1001 1010 1014 1017 1023 -8.087881
## 81 1004 1008 1011 1017 1025 -8.079184
## 82 1003 1007 1014 1020 1021 -8.061842
## 83 1003 1006 1014 1020 1022 -7.963107
## 84 1002 1010 1011 1019 1023 -7.921859
## 85 1001 1007 1015 1019 1023 -7.854816
## 86 1002 1009 1013 1020 1021 -7.848309
## 87 1005 1009 1013 1017 1021 -7.830720
## 88 1005 1006 1013 1019 1022 -7.795816
## 89 1003 1006 1012 1020 1024 -7.750362
## 90 1004 1010 1011 1017 1023 -7.711369
## 91 1002 1006 1013 1020 1024 -7.508292
## 92 1004 1007 1013 1020 1021 -7.439012
## 93 1001 1010 1012 1019 1023 -7.377810
## 94 1004 1006 1013 1020 1022 -7.377590
## 95 1001 1008 1015 1017 1024 -7.351056
## 96 1001 1009 1015 1017 1023 -7.339624
## 97 1005 1008 1011 1019 1022 -7.280906
## 98 1001 1009 1012 1020 1023 -7.229004
## 99 1001 1007 1014 1020 1023 -7.224164
## 100 1001 1008 1014 1020 1022 -7.195944
## 101 1005 1009 1011 1017 1023 -7.146015
## 102 1002 1009 1011 1020 1023 -7.108867
## 103 1001 1008 1012 1020 1024 -7.093905
## 104 1005 1008 1011 1017 1024 -6.980639
## 105 1002 1006 1013 1019 1025 -6.917538
## 106 1004 1007 1011 1020 1023 -6.894940
## 107 1002 1008 1015 1019 1021 -6.479077
## 108 1004 1008 1015 1017 1021 -6.291151
## 109 1004 1006 1015 1017 1023 -6.240906
## 110 1005 1008 1012 1019 1021 -6.226379
## 111 1005 1006 1012 1019 1023 -6.197888
## 112 1005 1008 1014 1017 1021 -6.018750
## 113 1002 1008 1011 1020 1024 -5.888738
## 114 1004 1008 1012 1020 1021 -5.863729
## 115 1004 1006 1012 1020 1023 -5.856217
## 116 1002 1006 1014 1020 1023 -5.843640
## 117 1002 1008 1014 1020 1021 -5.833166
## 118 1004 1008 1011 1020 1022 -4.867119
## 119 1005 1006 1014 1017 1023 -4.268147
## 120 1002 1006 1015 1019 1023 -2.912301
connection)
Click for ΔAIC and p-value dsitributions
hist(iterated_results_table$ΔAIC_fix, main = "Distribution of ΔAIC of the fixed model.") 
hist(iterated_results_table$p_fix, main = "Distribution of p-values of the fixed model.") 
model_stats_fixed = data.frame(
deltaAIC = mean(iterated_results_table$ΔAIC_fix),
p_value = mean(iterated_results_table$p_fix),
R2 = NA
)
model_stats_fixed %>%
mutate(deltaAIC = round(deltaAIC, digits = 1),
p_value = round(p_value, digits = 3),
R2 = NULL,
evidence = "",
evidence = ifelse(p_value > 0.1,
"none",
evidence),
evidence = ifelse(p_value < 0.1,
"* weak",
evidence),
evidence = ifelse(p_value < 0.05,
"** moderate",
evidence),
evidence = ifelse(p_value < 0.01,
"*** strong",
evidence),
evidence = ifelse(p_value < 0.001,
"**** very strong",
evidence),
p_value = ifelse(p_value < 0.001,
"< 0.001",
p_value)) %>%
print()## deltaAIC p_value evidence
## 1 2 0.483 noneresults_table = fill.results.table(
results_table,
response_variable,
metaecosystem_type_i,
model_stats_full,
model_stats_fixed
)MM (meta-ecosystems vs isolated)
metaecosystem_type_i = c("Medium-Medium meta-ecosystem",
"Medium-Medium isolated")filtered_data = ds_metaecosystems %>%
filter(time_point >= first_time_point_model,
time_point <= last_time_point_model,
metaecosystem_type %in% metaecosystem_type_i)plot.metaecos.points(filtered_data,
metaecosystem_type_i,
response_variable)
The p-values and ΔAIC of the connection are computed for all possible combinations of the isolated patches, creating a p-value and ΔAIC distributions. We keep as p-value and ΔAIC of the comparison the mean of such distributions.
Click for bootstrapping ΔAIC and p-value
If you see “Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel = ## 1e-15, : unused control arguments ignored”, Ignore it. It just means that you didn’t pass the control argument to the Nelder_Mead optimiser, so it uses the default.
# Compute stats for all patch combinations
isolated_combinations_sets_filtered = isolated_combinations_sets %>%
filter(disturbance == disturbance_global_input,
metaecosystem_type %in% metaecosystem_type_i)
n_sets = isolated_combinations_sets_filtered %>%
pull(set) %>%
max()
iterated_results_table = data.frame(
Response = as.character(NA),
Levels = as.character(NA),
ΔAIC_full = NA,
p_full = NA,
ΔR2_full = NA,
ΔAIC_fix = NA,
p_fix = NA,
ΔR2_fix = NA,
combination_set = NA,
system_nr_isolated_systems = as.character(NA)
) %>%
slice(-1)
for (set_input in 1:n_sets) {
system_nr_isolated_systems_input = isolated_combinations_sets_filtered %>%
filter(
metaecosystem_type %in% metaecosystem_type_i,
connection == "isolated",
set == set_input
) %>%
pull(system_nr)
filtered_data = ds_metaecosystems %>%
filter(
time_point >= first_time_point_model,
time_point <= last_time_point_model,
metaecosystem_type %in% metaecosystem_type_i,
connection == "connected" |
(connection == "isolated" &
system_nr %in% system_nr_isolated_systems_input)
)
null_model = lmer(
get(response_variable) ~
day +
(day | system_nr),
data = filtered_data,
REML = FALSE,
control = lmerControl(optimizer = optimizer_input,
optCtrl = list(method = method_input))
)
full_model = lmer(
get(response_variable) ~
day +
connection +
connection:day +
(day | system_nr),
data = filtered_data,
REML = FALSE,
control = lmerControl(optimizer = optimizer_input,
optCtrl = list(method = method_input))
)
model_stats_full = compute.model.stats(full_model,
null_model,
"mixed_model")
reduced_model = lmer(
get(response_variable) ~
day +
connection +
(day | system_nr),
data = filtered_data,
REML = FALSE,
control = lmerControl(optimizer = optimizer_input,
optCtrl = list(method = method_input))
)
model_stats_fixed = compute.model.stats(reduced_model,
null_model,
"mixed_model")
iterated_results_table = fill.results.table(
iterated_results_table,
response_variable,
metaecosystem_type_i,
model_stats_full,
model_stats_fixed
)
iterated_results_table$set[nrow(iterated_results_table)] = set_input
iterated_results_table$system_nr_isolated_systems[nrow(iterated_results_table)] = paste(system_nr_isolated_systems_input, collapse = " ")
}## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
connection and connection : day)
Click for ΔAIC and p-value distributions
hist(iterated_results_table$ΔAIC_full, main = "Distribution of ΔAIC of the full model.") 
hist(iterated_results_table$p_full, main = "Distribution of p-values of the full model.") 
model_stats_full = data.frame(
deltaAIC = mean(iterated_results_table$ΔAIC_full),
p_value = mean(iterated_results_table$p_full),
R2 = NA
)
model_stats_full %>%
mutate(deltaAIC = round(deltaAIC, digits = 1),
p_value = round(p_value, digits = 3),
R2 = NULL,
evidence = "",
evidence = ifelse(p_value > 0.1,
"none",
evidence),
evidence = ifelse(p_value < 0.1,
"* weak",
evidence),
evidence = ifelse(p_value < 0.05,
"** moderate",
evidence),
evidence = ifelse(p_value < 0.01,
"*** strong",
evidence),
evidence = ifelse(p_value < 0.001,
"**** very strong",
evidence),
p_value = ifelse(p_value < 0.001,
"< 0.001",
p_value)) %>%
print()## deltaAIC p_value evidence
## 1 2.9 0.591 noneClick for which system nr produced which AIC in the full model
iterated_results_table %>%
select(system_nr_isolated_systems,
ΔAIC_full) %>%
arrange(ΔAIC_full)## system_nr_isolated_systems ΔAIC_full
## 1 1048 1054 2.250274
## 2 1054 1048 2.250274
## 3 1048 1050 2.252695
## 4 1050 1048 2.252695
## 5 1046 1053 2.329635
## 6 1053 1046 2.329635
## 7 1047 1051 2.383022
## 8 1051 1047 2.383022
## 9 1049 1053 2.417776
## 10 1053 1049 2.417776
## 11 1047 1055 2.531994
## 12 1055 1047 2.531994
## 13 1046 1054 2.677725
## 14 1054 1046 2.677725
## 15 1049 1050 2.768293
## 16 1050 1049 2.768293
## 17 1047 1052 2.932105
## 18 1052 1047 2.932105
## 19 1051 1054 3.103515
## 20 1054 1051 3.103515
## 21 1050 1055 3.167263
## 22 1055 1050 3.167263
## 23 1052 1053 3.237364
## 24 1053 1052 3.237364
## 25 1049 1051 3.697185
## 26 1051 1049 3.697185
## 27 1046 1055 3.706302
## 28 1055 1046 3.706302
## 29 1048 1052 3.711991
## 30 1052 1048 3.711991
connection)
Click for ΔAIC and p-value dsitributions
hist(iterated_results_table$ΔAIC_fix, main = "Distribution of ΔAIC of the fixed model.") 
hist(iterated_results_table$p_fix, main = "Distribution of p-values of the fixed model.") 
model_stats_fixed = data.frame(
deltaAIC = mean(iterated_results_table$ΔAIC_fix),
p_value = mean(iterated_results_table$p_fix),
R2 = NA
)
model_stats_fixed %>%
mutate(deltaAIC = round(deltaAIC, digits = 1),
p_value = round(p_value, digits = 3),
R2 = NULL,
evidence = "",
evidence = ifelse(p_value > 0.1,
"none",
evidence),
evidence = ifelse(p_value < 0.1,
"* weak",
evidence),
evidence = ifelse(p_value < 0.05,
"** moderate",
evidence),
evidence = ifelse(p_value < 0.01,
"*** strong",
evidence),
evidence = ifelse(p_value < 0.001,
"**** very strong",
evidence),
p_value = ifelse(p_value < 0.001,
"< 0.001",
p_value)) %>%
print()## deltaAIC p_value evidence
## 1 1.9 0.788 noneresults_table = fill.results.table(
results_table,
response_variable,
metaecosystem_type_i,
model_stats_full,
model_stats_fixed
)Beta (Bray-Curtis)
response_variable = "bray_curtis"metaecosystem_type_i = c("Medium-Medium meta-ecosystem",
"Medium-Medium isolated",
"Small-Large meta-ecosystem",
"Small-Large isolated")plot.metaecos.points(ds_metaecosystems,
metaecosystem_type_i,
response_variable,
3)
Boxplots
plot.metaecos.boxplots(ds_metaecosystems,
metaecosystem_type_i,
response_variable)
Replicates
plot.metaecos.replicates(ds_metaecosystems,
metaecosystem_type_i,
response_variable)
MM vs SL meta-ecosystems
metaecosystem_type_i = c("Medium-Medium meta-ecosystem",
"Small-Large meta-ecosystem")filtered_data = ds_metaecosystems %>%
filter(time_point >= first_time_point_model,
time_point <= last_time_point_model,
metaecosystem_type %in% metaecosystem_type_i)plot.metaecos.points(filtered_data,
metaecosystem_type_i,
response_variable)
null_model = lmer(
get(response_variable) ~
day +
(day | system_nr),
data = filtered_data,
REML = FALSE,
control = lmerControl(optimizer = "Nelder_Mead")
)patch_size_symmetry and
patch_size_symmetry : day)
full_model = lmer(
get(response_variable) ~
day +
patch_size_symmetry +
patch_size_symmetry : day +
(day | system_nr),
data = filtered_data,
REML = FALSE,
control = lmerControl(optimizer = "Nelder_Mead")
)Click for model output
print(summary(full_model), digits = 1)## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
## method [lmerModLmerTest]
## Formula:
## get(response_variable) ~ day + patch_size_symmetry + patch_size_symmetry:day +
## (day | system_nr)
## Data: filtered_data
## Control: lmerControl(optimizer = "Nelder_Mead")
##
## AIC BIC logLik deviance df.resid
## -68.4 -51.6 42.2 -84.4 52
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.33 -0.58 -0.03 0.63 2.01
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## system_nr (Intercept) 3e-13 5e-07
## day 9e-16 3e-08 -1.00
## Residual 1e-02 1e-01
## Number of obs: 60, groups: system_nr, 10
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.360 0.062 60.000 5.8 2e-07
## day 0.007 0.003 60.000 2.1 0.04
## patch_size_symmetrysymmetric 0.047 0.087 60.000 0.5 0.59
## day:patch_size_symmetrysymmetric -0.006 0.005 60.000 -1.3 0.21
##
## (Intercept) ***
## day *
## patch_size_symmetrysymmetric
## day:patch_size_symmetrysymmetric
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) day ptch__
## day -0.935
## ptch_sz_sym -0.707 0.661
## dy:ptch_sz_ 0.661 -0.707 -0.935
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')#Create resid vs fitted
res_vs_fit = filtered_data %>%
mutate(predicted = fitted(full_model),
residuals = resid(full_model)) %>%
plot_ly(x = ~predicted,
y = ~residuals,
type = "scatter",
mode = "markers",
marker = list(size = 5, color = "#4C78A8"),
text = paste(" System nr: ", filtered_data$system_nr, "<br>",
"Day: ", filtered_data$day, "<br>",
"Meta-ecosystem: ", filtered_data$metaecosystem_type, "<br>",
"Tot Biomass: ", round(filtered_data$total_metaecosystem_bioarea_mm2, digits = 0), "<br>"),
hoverinfo = "text") %>%
plotly::layout(
xaxis = list(title = "Fitted Values"),
yaxis = list(title = "Residuals")
)Click for residual plots
res_vs_fitqqnorm(resid(full_model))
model_stats_full = compute.model.stats(full_model,
null_model,
"mixed_model")
model_stats_full %>%
mutate(deltaAIC = round(deltaAIC, digits = 1),
p_value = round(p_value, digits = 3),
R2 = NULL,
evidence = "",
evidence = ifelse(p_value > 0.1,
"none",
evidence),
evidence = ifelse(p_value < 0.1,
"* weak",
evidence),
evidence = ifelse(p_value < 0.05,
"** moderate",
evidence),
evidence = ifelse(p_value < 0.01,
"*** strong",
evidence),
evidence = ifelse(p_value < 0.001,
"**** very strong",
evidence),
p_value = ifelse(p_value < 0.001,
"< 0.001",
p_value)) %>%
print()## deltaAIC p_value evidence
## 1 -0.6 0.099 * weak
patch_size_symmetry)
reduced_model = lmer(
get(response_variable) ~
day +
patch_size_symmetry +
(day | system_nr),
data = filtered_data,
REML = FALSE,
control = lmerControl(optimizer = "Nelder_Mead")
)Click for model output
print(summary(reduced_model), digits = 1)## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
## method [lmerModLmerTest]
## Formula: get(response_variable) ~ day + patch_size_symmetry + (day | system_nr)
## Data: filtered_data
## Control: lmerControl(optimizer = "Nelder_Mead")
##
## AIC BIC logLik deviance df.resid
## -68.8 -54.1 41.4 -82.8 53
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.54 -0.67 -0.04 0.66 1.93
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## system_nr (Intercept) 6e-13 8e-07
## day 1e-15 3e-08 -1.00
## Residual 1e-02 1e-01
## Number of obs: 60, groups: system_nr, 10
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.411 0.047 60.000 9 2e-12 ***
## day 0.004 0.002 60.000 2 0.10 .
## patch_size_symmetrysymmetric -0.056 0.031 60.000 -2 0.08 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) day
## day -0.881
## ptch_sz_sym -0.334 0.000
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')#Create resid vs fitted
res_vs_fit = filtered_data %>%
mutate(predicted = fitted(reduced_model),
residuals = resid(reduced_model)) %>%
plot_ly(x = ~predicted,
y = ~residuals,
type = "scatter",
mode = "markers",
marker = list(size = 5, color = "#4C78A8"),
text = paste(" System nr: ", filtered_data$system_nr, "<br>",
"Day: ", filtered_data$day, "<br>",
"Meta-ecosystem: ", filtered_data$metaecosystem_type, "<br>",
"Tot Biomass: ", round(filtered_data$total_metaecosystem_bioarea_mm2, digits = 0), "<br>"),
hoverinfo = "text") %>%
plotly::layout(
xaxis = list(title = "Fitted Values"),
yaxis = list(title = "Residuals")
)Click for residual plots
res_vs_fitqqnorm(resid(reduced_model))
model_stats_fixed = compute.model.stats(reduced_model,
null_model,
"mixed_model")
model_stats_fixed %>%
mutate(deltaAIC = round(deltaAIC, digits = 1),
p_value = round(p_value, digits = 3),
R2 = NULL,
evidence = "",
evidence = ifelse(p_value > 0.1,
"none",
evidence),
evidence = ifelse(p_value < 0.1,
"* weak",
evidence),
evidence = ifelse(p_value < 0.05,
"** moderate",
evidence),
evidence = ifelse(p_value < 0.01,
"*** strong",
evidence),
evidence = ifelse(p_value < 0.001,
"**** very strong",
evidence),
p_value = ifelse(p_value < 0.001,
"< 0.001",
p_value)) %>%
print()## deltaAIC p_value evidence
## 1 -1.1 0.08 * weakresults_table = fill.results.table(
results_table,
response_variable,
metaecosystem_type_i,
model_stats_full,
model_stats_fixed
)SL (meta-ecosystems vs isolated)
metaecosystem_type_i = c("Small-Large meta-ecosystem",
"Small-Large isolated")filtered_data = ds_metaecosystems %>%
filter(time_point >= first_time_point_model,
time_point <= last_time_point_model,
metaecosystem_type %in% metaecosystem_type_i)plot.metaecos.points(filtered_data,
metaecosystem_type_i,
response_variable)
The p-values and ΔAIC of the connection are computed for all possible combinations of the isolated patches, creating a p-value and ΔAIC distributions. We keep as p-value and ΔAIC of the comparison the mean of such distributions.
Click for bootstrapping ΔAIC and p-value
If you see “Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel = ## 1e-15, : unused control arguments ignored”, Ignore it. It just means that you didn’t pass the control argument to the Nelder_Mead optimiser, so it uses the default.
# Compute stats for all patch combinations
isolated_combinations_sets_filtered = isolated_combinations_sets %>%
filter(disturbance == disturbance_global_input,
metaecosystem_type %in% metaecosystem_type_i)
n_sets = isolated_combinations_sets_filtered %>%
pull(set) %>%
max()
iterated_results_table = data.frame(
Response = as.character(NA),
Levels = as.character(NA),
ΔAIC_full = NA,
p_full = NA,
ΔR2_full = NA,
ΔAIC_fix = NA,
p_fix = NA,
ΔR2_fix = NA,
combination_set = NA,
system_nr_isolated_systems = as.character(NA)
) %>%
slice(-1)
for (set_input in 1:n_sets) {
system_nr_isolated_systems_input = isolated_combinations_sets_filtered %>%
filter(
metaecosystem_type %in% metaecosystem_type_i,
connection == "isolated",
set == set_input
) %>%
pull(system_nr)
filtered_data = ds_metaecosystems %>%
filter(
time_point >= first_time_point_model,
time_point <= last_time_point_model,
metaecosystem_type %in% metaecosystem_type_i,
connection == "connected" |
(connection == "isolated" &
system_nr %in% system_nr_isolated_systems_input)
)
null_model = lmer(
get(response_variable) ~
day +
(day | system_nr),
data = filtered_data,
REML = FALSE,
control = lmerControl(optimizer = optimizer_input,
optCtrl = list(method = method_input))
)
full_model = lmer(
get(response_variable) ~
day +
connection +
connection:day +
(day | system_nr),
data = filtered_data,
REML = FALSE,
control = lmerControl(optimizer = optimizer_input,
optCtrl = list(method = method_input))
)
model_stats_full = compute.model.stats(full_model,
null_model,
"mixed_model")
reduced_model = lmer(
get(response_variable) ~
day +
connection +
(day | system_nr),
data = filtered_data,
REML = FALSE,
control = lmerControl(optimizer = optimizer_input,
optCtrl = list(method = method_input))
)
model_stats_fixed = compute.model.stats(reduced_model,
null_model,
"mixed_model")
iterated_results_table = fill.results.table(
iterated_results_table,
response_variable,
metaecosystem_type_i,
model_stats_full,
model_stats_fixed
)
iterated_results_table$set[nrow(iterated_results_table)] = set_input
iterated_results_table$system_nr_isolated_systems[nrow(iterated_results_table)] = paste(system_nr_isolated_systems_input, collapse = " ")
}## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -3.4e+02## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -2.5e+02## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -2.4e+02## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -3.3e+02## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -3.5e+00## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -2.3e+02## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -7.7e-01## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -7.1e-02## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -3.6e+01## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -3.1e+02## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -3.3e+02## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -3.2e+02## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -3.3e+02## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -2.9e+02## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -3.0e+02## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -7.2e+01## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -3.0e+02## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -7.5e+00## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -3.1e+02## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -1.2e+02## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -1.7e+01## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -3.8e+02## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -3.7e+02## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -2.6e+02## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -3.2e+02## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -2.4e+02## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -2.2e+02## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -3.2e+02## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -3.0e+02## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## unable to evaluate scaled gradient## Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
## Model failed to converge: degenerate Hessian with 1 negative eigenvalues## Warning: Model failed to converge with 1 negative eigenvalue: -3.8e+02## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -1.4e+02## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -3.4e+02## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -1.3e+01## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -3.4e+02## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
connection and connection : day)
Click for ΔAIC and p-value distributions
hist(iterated_results_table$ΔAIC_full, main = "Distribution of ΔAIC of the full model.") 
hist(iterated_results_table$p_full, main = "Distribution of p-values of the full model.") 
model_stats_full = data.frame(
deltaAIC = mean(iterated_results_table$ΔAIC_full),
p_value = mean(iterated_results_table$p_full),
R2 = NA
)
model_stats_full %>%
mutate(deltaAIC = round(deltaAIC, digits = 1),
p_value = round(p_value, digits = 3),
R2 = NULL,
evidence = "",
evidence = ifelse(p_value > 0.1,
"none",
evidence),
evidence = ifelse(p_value < 0.1,
"* weak",
evidence),
evidence = ifelse(p_value < 0.05,
"** moderate",
evidence),
evidence = ifelse(p_value < 0.01,
"*** strong",
evidence),
evidence = ifelse(p_value < 0.001,
"**** very strong",
evidence),
p_value = ifelse(p_value < 0.001,
"< 0.001",
p_value)) %>%
print()## deltaAIC p_value evidence
## 1 -12.4 0.007 *** strongClick for which system nr produced which AIC in the full model
iterated_results_table %>%
select(system_nr_isolated_systems,
ΔAIC_full) %>%
arrange(ΔAIC_full)## system_nr_isolated_systems ΔAIC_full
## 1 1003 1007 1015 1016 1024 -23.6032253
## 2 1002 1008 1015 1016 1024 -22.3439447
## 3 1005 1008 1012 1016 1024 -22.2510431
## 4 1005 1008 1011 1019 1022 -21.2082743
## 5 1005 1006 1013 1019 1022 -20.6450651
## 6 1003 1010 1011 1019 1022 -19.9619334
## 7 1001 1007 1013 1019 1025 -19.7922111
## 8 1002 1006 1013 1019 1025 -19.4185919
## 9 1005 1007 1011 1018 1024 -19.3463546
## 10 1003 1007 1011 1020 1024 -19.1386767
## 11 1002 1008 1015 1019 1021 -19.0089440
## 12 1002 1008 1011 1020 1024 -18.3564894
## 13 1002 1010 1011 1018 1024 -18.3193147
## 14 1005 1008 1011 1017 1024 -18.2289310
## 15 1005 1007 1013 1016 1024 -18.1285187
## 16 1003 1010 1011 1017 1024 -17.9028991
## 17 1003 1010 1012 1019 1021 -17.7820316
## 18 1001 1007 1013 1020 1024 -17.5583143
## 19 1002 1006 1013 1020 1024 -17.4044416
## 20 1001 1007 1015 1018 1024 -17.3703409
## 21 1002 1006 1015 1018 1024 -17.2704297
## 22 1005 1006 1013 1017 1024 -17.2440605
## 23 1002 1010 1013 1016 1024 -17.1188911
## 24 1005 1006 1012 1018 1024 -17.1174820
## 25 1003 1006 1012 1020 1024 -17.0081684
## 26 1002 1009 1015 1018 1021 -16.9812430
## 27 1003 1006 1015 1017 1024 -16.9347849
## 28 1004 1006 1015 1018 1022 -16.8607485
## 29 1003 1010 1012 1016 1024 -16.8123348
## 30 1002 1009 1015 1016 1023 -16.7612477
## 31 1005 1009 1012 1018 1021 -16.6485319
## 32 1001 1010 1013 1017 1024 -16.5039406
## 33 1001 1008 1012 1020 1024 -16.4769424
## 34 1001 1010 1012 1018 1024 -16.4289791
## 35 1001 1008 1015 1017 1024 -16.4088006
## 36 1004 1007 1013 1016 1025 -16.3410232
## 37 1001 1009 1015 1017 1023 -15.9373692
## 38 1005 1006 1014 1018 1022 -15.2619106
## 39 1003 1007 1014 1016 1025 -14.8284133
## 40 1003 1010 1014 1016 1022 -14.5355625
## 41 1001 1008 1014 1020 1022 -14.5155532
## 42 1001 1007 1014 1018 1025 -14.4583717
## 43 1003 1007 1011 1019 1025 -14.1882582
## 44 1005 1007 1013 1019 1021 -14.1263198
## 45 1004 1010 1012 1016 1023 -14.0523185
## 46 1003 1007 1015 1019 1021 -13.9325971
## 47 1005 1007 1014 1018 1021 -13.8480044
## 48 1002 1008 1011 1019 1025 -13.8315706
## 49 1002 1010 1013 1019 1021 -13.8167285
## 50 1001 1007 1014 1020 1023 -13.5917284
## 51 1005 1007 1011 1019 1023 -13.3398966
## 52 1003 1006 1015 1019 1022 -13.2539201
## 53 1005 1008 1012 1019 1021 -13.2038251
## 54 1003 1006 1012 1019 1025 -13.1804566
## 55 1002 1010 1011 1019 1023 -13.0049436
## 56 1001 1010 1013 1019 1022 -12.8601572
## 57 1001 1008 1015 1019 1022 -12.5344105
## 58 1005 1009 1011 1018 1022 -12.4779641
## 59 1002 1009 1011 1018 1025 -12.4220539
## 60 1003 1009 1011 1020 1022 -12.4110411
## 61 1002 1009 1013 1020 1021 -12.4007121
## 62 1001 1008 1012 1019 1025 -12.3920125
## 63 1002 1006 1015 1019 1023 -12.3894220
## 64 1003 1009 1011 1017 1025 -12.2646293
## 65 1005 1009 1013 1017 1021 -12.1939510
## 66 1005 1006 1012 1019 1023 -12.0869860
## 67 1003 1009 1012 1020 1021 -11.9974231
## 68 1003 1009 1015 1017 1021 -11.9857526
## 69 1001 1007 1015 1019 1023 -11.9330563
## 70 1002 1009 1013 1016 1025 -11.7566153
## 71 1002 1009 1011 1020 1023 -11.6341627
## 72 1005 1009 1013 1016 1022 -11.5897457
## 73 1001 1010 1012 1019 1023 -11.5394740
## 74 1003 1009 1012 1016 1025 -11.5007515
## 75 1001 1009 1013 1020 1022 -11.4343658
## 76 1005 1009 1011 1017 1023 -11.3995681
## 77 1003 1009 1015 1016 1022 -11.3951793
## 78 1001 1009 1013 1017 1025 -11.3487519
## 79 1001 1009 1015 1018 1022 -11.1684533
## 80 1001 1009 1012 1018 1025 -11.1293018
## 81 1005 1009 1012 1016 1023 -10.4671771
## 82 1001 1009 1012 1020 1023 -10.3425093
## 83 1003 1007 1014 1020 1021 -9.8142296
## 84 1003 1006 1014 1020 1022 -9.7527196
## 85 1002 1006 1014 1018 1025 -9.5720835
## 86 1002 1010 1014 1018 1021 -9.5506955
## 87 1002 1008 1014 1020 1021 -9.5111077
## 88 1003 1006 1014 1017 1025 -9.4071616
## 89 1002 1008 1014 1016 1025 -9.3819928
## 90 1005 1008 1014 1016 1022 -9.3195634
## 91 1003 1010 1014 1017 1021 -9.3064732
## 92 1001 1010 1014 1018 1022 -9.2439662
## 93 1005 1008 1014 1017 1021 -9.2187726
## 94 1002 1006 1014 1020 1023 -8.9893260
## 95 1001 1008 1014 1017 1025 -8.8180065
## 96 1005 1007 1014 1016 1023 -8.8136450
## 97 1002 1010 1014 1016 1023 -8.7412800
## 98 1005 1006 1014 1017 1023 -8.6489524
## 99 1001 1010 1014 1017 1023 -8.2893284
## 100 1004 1010 1011 1018 1022 -5.6962742
## 101 1004 1007 1011 1018 1025 -5.6855647
## 102 1004 1008 1011 1020 1022 -5.6382083
## 103 1004 1007 1011 1020 1023 -5.4994234
## 104 1004 1008 1011 1017 1025 -5.2449966
## 105 1004 1007 1013 1020 1021 -5.1384486
## 106 1004 1010 1011 1017 1023 -5.1268641
## 107 1004 1007 1015 1018 1021 -4.8776450
## 108 1004 1010 1013 1017 1021 -4.8303304
## 109 1004 1010 1013 1016 1022 -4.6567662
## 110 1004 1008 1015 1017 1021 -4.5459723
## 111 1004 1010 1012 1018 1021 -4.5143183
## 112 1004 1008 1012 1020 1021 -4.4532780
## 113 1004 1008 1015 1016 1022 -4.3798257
## 114 1004 1006 1012 1018 1025 -4.3630675
## 115 1004 1007 1015 1016 1023 -4.1999584
## 116 1004 1006 1012 1020 1023 -4.1700004
## 117 1004 1008 1012 1016 1025 -4.0607610
## 118 1004 1006 1013 1017 1025 0.5317163
## 119 1004 1006 1013 1020 1022 0.8298978
## 120 1004 1006 1015 1017 1023 1.1434381
connection)
Click for ΔAIC and p-value dsitributions
hist(iterated_results_table$ΔAIC_fix, main = "Distribution of ΔAIC of the fixed model.") 
hist(iterated_results_table$p_fix, main = "Distribution of p-values of the fixed model.") 
model_stats_fixed = data.frame(
deltaAIC = mean(iterated_results_table$ΔAIC_fix),
p_value = mean(iterated_results_table$p_fix),
R2 = NA
)
model_stats_fixed %>%
mutate(deltaAIC = round(deltaAIC, digits = 1),
p_value = round(p_value, digits = 3),
R2 = NULL,
evidence = "",
evidence = ifelse(p_value > 0.1,
"none",
evidence),
evidence = ifelse(p_value < 0.1,
"* weak",
evidence),
evidence = ifelse(p_value < 0.05,
"** moderate",
evidence),
evidence = ifelse(p_value < 0.01,
"*** strong",
evidence),
evidence = ifelse(p_value < 0.001,
"**** very strong",
evidence),
p_value = ifelse(p_value < 0.001,
"< 0.001",
p_value)) %>%
print()## deltaAIC p_value evidence
## 1 -3.6 0.075 * weakresults_table = fill.results.table(
results_table,
response_variable,
metaecosystem_type_i,
model_stats_full,
model_stats_fixed
)MM (meta-ecosystems vs isolated)
metaecosystem_type_i = c("Medium-Medium meta-ecosystem",
"Medium-Medium isolated")filtered_data = ds_metaecosystems %>%
filter(time_point >= first_time_point_model,
time_point <= last_time_point_model,
metaecosystem_type %in% metaecosystem_type_i)plot.metaecos.points(filtered_data,
metaecosystem_type_i,
response_variable)
The p-values and ΔAIC of the connection are computed for all possible combinations of the isolated patches, creating a p-value and ΔAIC distributions. We keep as p-value and ΔAIC of the comparison the mean of such distributions.
Click for bootstrapping ΔAIC and p-value
If you see “Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel = ## 1e-15, : unused control arguments ignored”, Ignore it. It just means that you didn’t pass the control argument to the Nelder_Mead optimiser, so it uses the default.
# Compute stats for all patch combinations
isolated_combinations_sets_filtered = isolated_combinations_sets %>%
filter(disturbance == disturbance_global_input,
metaecosystem_type %in% metaecosystem_type_i)
n_sets = isolated_combinations_sets_filtered %>%
pull(set) %>%
max()
iterated_results_table = data.frame(
Response = as.character(NA),
Levels = as.character(NA),
ΔAIC_full = NA,
p_full = NA,
ΔR2_full = NA,
ΔAIC_fix = NA,
p_fix = NA,
ΔR2_fix = NA,
combination_set = NA,
system_nr_isolated_systems = as.character(NA)
) %>%
slice(-1)
for (set_input in 1:n_sets) {
system_nr_isolated_systems_input = isolated_combinations_sets_filtered %>%
filter(
metaecosystem_type %in% metaecosystem_type_i,
connection == "isolated",
set == set_input
) %>%
pull(system_nr)
filtered_data = ds_metaecosystems %>%
filter(
time_point >= first_time_point_model,
time_point <= last_time_point_model,
metaecosystem_type %in% metaecosystem_type_i,
connection == "connected" |
(connection == "isolated" &
system_nr %in% system_nr_isolated_systems_input)
)
null_model = lmer(
get(response_variable) ~
day +
(day | system_nr),
data = filtered_data,
REML = FALSE,
control = lmerControl(optimizer = optimizer_input,
optCtrl = list(method = method_input))
)
full_model = lmer(
get(response_variable) ~
day +
connection +
connection:day +
(day | system_nr),
data = filtered_data,
REML = FALSE,
control = lmerControl(optimizer = optimizer_input,
optCtrl = list(method = method_input))
)
model_stats_full = compute.model.stats(full_model,
null_model,
"mixed_model")
reduced_model = lmer(
get(response_variable) ~
day +
connection +
(day | system_nr),
data = filtered_data,
REML = FALSE,
control = lmerControl(optimizer = optimizer_input,
optCtrl = list(method = method_input))
)
model_stats_fixed = compute.model.stats(reduced_model,
null_model,
"mixed_model")
iterated_results_table = fill.results.table(
iterated_results_table,
response_variable,
metaecosystem_type_i,
model_stats_full,
model_stats_fixed
)
iterated_results_table$set[nrow(iterated_results_table)] = set_input
iterated_results_table$system_nr_isolated_systems[nrow(iterated_results_table)] = paste(system_nr_isolated_systems_input, collapse = " ")
}## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
connection and connection : day)
Click for ΔAIC and p-value distributions
hist(iterated_results_table$ΔAIC_full, main = "Distribution of ΔAIC of the full model.") 
hist(iterated_results_table$p_full, main = "Distribution of p-values of the full model.") 
model_stats_full = data.frame(
deltaAIC = mean(iterated_results_table$ΔAIC_full),
p_value = mean(iterated_results_table$p_full),
R2 = NA
)
model_stats_full %>%
mutate(deltaAIC = round(deltaAIC, digits = 1),
p_value = round(p_value, digits = 3),
R2 = NULL,
evidence = "",
evidence = ifelse(p_value > 0.1,
"none",
evidence),
evidence = ifelse(p_value < 0.1,
"* weak",
evidence),
evidence = ifelse(p_value < 0.05,
"** moderate",
evidence),
evidence = ifelse(p_value < 0.01,
"*** strong",
evidence),
evidence = ifelse(p_value < 0.001,
"**** very strong",
evidence),
p_value = ifelse(p_value < 0.001,
"< 0.001",
p_value)) %>%
print()## deltaAIC p_value evidence
## 1 0.8 0.308 noneClick for which system nr produced which AIC in the full model
iterated_results_table %>%
select(system_nr_isolated_systems,
ΔAIC_full) %>%
arrange(ΔAIC_full)## system_nr_isolated_systems ΔAIC_full
## 1 1047 1052 -2.9518507
## 2 1052 1047 -2.9518507
## 3 1050 1055 -2.1310860
## 4 1055 1050 -2.1310860
## 5 1047 1051 -2.0992457
## 6 1051 1047 -2.0992457
## 7 1047 1055 -1.6448276
## 8 1055 1047 -1.6448276
## 9 1052 1053 -1.3285235
## 10 1053 1052 -1.3285235
## 11 1046 1055 0.3823059
## 12 1055 1046 0.3823059
## 13 1048 1050 1.6910361
## 14 1050 1048 1.6910361
## 15 1046 1053 1.8190972
## 16 1053 1046 1.8190972
## 17 1049 1051 1.9617634
## 18 1051 1049 1.9617634
## 19 1051 1054 1.9863126
## 20 1054 1051 1.9863126
## 21 1049 1053 2.0137185
## 22 1053 1049 2.0137185
## 23 1049 1050 2.0657045
## 24 1050 1049 2.0657045
## 25 1048 1052 2.5035401
## 26 1052 1048 2.5035401
## 27 1046 1054 3.1677620
## 28 1054 1046 3.1677620
## 29 1048 1054 3.8923779
## 30 1054 1048 3.8923779
connection)
Click for ΔAIC and p-value dsitributions
hist(iterated_results_table$ΔAIC_fix, main = "Distribution of ΔAIC of the fixed model.") 
hist(iterated_results_table$p_fix, main = "Distribution of p-values of the fixed model.") 
model_stats_fixed = data.frame(
deltaAIC = mean(iterated_results_table$ΔAIC_fix),
p_value = mean(iterated_results_table$p_fix),
R2 = NA
)
model_stats_fixed %>%
mutate(deltaAIC = round(deltaAIC, digits = 1),
p_value = round(p_value, digits = 3),
R2 = NULL,
evidence = "",
evidence = ifelse(p_value > 0.1,
"none",
evidence),
evidence = ifelse(p_value < 0.1,
"* weak",
evidence),
evidence = ifelse(p_value < 0.05,
"** moderate",
evidence),
evidence = ifelse(p_value < 0.01,
"*** strong",
evidence),
evidence = ifelse(p_value < 0.001,
"**** very strong",
evidence),
p_value = ifelse(p_value < 0.001,
"< 0.001",
p_value)) %>%
print()## deltaAIC p_value evidence
## 1 -0.5 0.236 noneresults_table = fill.results.table(
results_table,
response_variable,
metaecosystem_type_i,
model_stats_full,
model_stats_fixed
)Gamma (regional richness)
response_variable = "metaecosystem_richness"metaecosystem_type_i = c("Medium-Medium meta-ecosystem",
"Medium-Medium isolated",
"Small-Large meta-ecosystem",
"Small-Large isolated")plot.metaecos.points(ds_metaecosystems,
metaecosystem_type_i,
response_variable,
3)
Boxplots
plot.metaecos.boxplots(ds_metaecosystems,
metaecosystem_type_i,
response_variable)
Replicates
plot.metaecos.replicates(ds_metaecosystems,
metaecosystem_type_i,
response_variable)
MM vs SL meta-ecosystems
metaecosystem_type_i = c("Medium-Medium meta-ecosystem",
"Small-Large meta-ecosystem")filtered_data = ds_metaecosystems %>%
filter(time_point >= first_time_point_model,
time_point <= last_time_point_model,
metaecosystem_type %in% metaecosystem_type_i)plot.metaecos.points(filtered_data,
metaecosystem_type_i,
response_variable)
null_model = lmer(
get(response_variable) ~
day +
(day | system_nr),
data = filtered_data,
REML = FALSE,
control = lmerControl(optimizer = "Nelder_Mead")
)patch_size_symmetry and
patch_size_symmetry : day)
full_model = lmer(
get(response_variable) ~
day +
patch_size_symmetry +
patch_size_symmetry : day +
(day | system_nr),
data = filtered_data,
REML = FALSE,
control = lmerControl(optimizer = "Nelder_Mead")
)Click for model output
print(summary(full_model), digits = 1)## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
## method [lmerModLmerTest]
## Formula:
## get(response_variable) ~ day + patch_size_symmetry + patch_size_symmetry:day +
## (day | system_nr)
## Data: filtered_data
## Control: lmerControl(optimizer = "Nelder_Mead")
##
## AIC BIC logLik deviance df.resid
## 206.3 223.1 -95.2 190.3 52
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.4 -0.6 0.1 0.7 1.6
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## system_nr (Intercept) 0.053 0.23
## day 0.002 0.04 -1.00
## Residual 1.181 1.09
## Number of obs: 60, groups: system_nr, 10
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 8.08 0.57 38.23 14.2 <2e-16 ***
## day -0.01 0.03 15.06 -0.3 0.8
## patch_size_symmetrysymmetric 0.46 0.80 38.23 0.6 0.6
## day:patch_size_symmetrysymmetric -0.01 0.05 15.06 -0.3 0.8
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) day ptch__
## day -0.874
## ptch_sz_sym -0.707 0.618
## dy:ptch_sz_ 0.618 -0.707 -0.874
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')#Create resid vs fitted
res_vs_fit = filtered_data %>%
mutate(predicted = fitted(full_model),
residuals = resid(full_model)) %>%
plot_ly(x = ~predicted,
y = ~residuals,
type = "scatter",
mode = "markers",
marker = list(size = 5, color = "#4C78A8"),
text = paste(" System nr: ", filtered_data$system_nr, "<br>",
"Day: ", filtered_data$day, "<br>",
"Meta-ecosystem: ", filtered_data$metaecosystem_type, "<br>",
"Tot Biomass: ", round(filtered_data$total_metaecosystem_bioarea_mm2, digits = 0), "<br>"),
hoverinfo = "text") %>%
plotly::layout(
xaxis = list(title = "Fitted Values"),
yaxis = list(title = "Residuals")
)Click for residual plots
res_vs_fitqqnorm(resid(full_model))
model_stats_full = compute.model.stats(full_model,
null_model,
"mixed_model")
model_stats_full %>%
mutate(deltaAIC = round(deltaAIC, digits = 1),
p_value = round(p_value, digits = 3),
R2 = NULL,
evidence = "",
evidence = ifelse(p_value > 0.1,
"none",
evidence),
evidence = ifelse(p_value < 0.1,
"* weak",
evidence),
evidence = ifelse(p_value < 0.05,
"** moderate",
evidence),
evidence = ifelse(p_value < 0.01,
"*** strong",
evidence),
evidence = ifelse(p_value < 0.001,
"**** very strong",
evidence),
p_value = ifelse(p_value < 0.001,
"< 0.001",
p_value)) %>%
print()## deltaAIC p_value evidence
## 1 3.4 0.752 none
patch_size_symmetry)
reduced_model = lmer(
get(response_variable) ~
day +
patch_size_symmetry +
(day | system_nr),
data = filtered_data,
REML = FALSE,
control = lmerControl(optimizer = "Nelder_Mead")
)Click for model output
print(summary(reduced_model), digits = 1)## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
## method [lmerModLmerTest]
## Formula: get(response_variable) ~ day + patch_size_symmetry + (day | system_nr)
## Data: filtered_data
## Control: lmerControl(optimizer = "Nelder_Mead")
##
## AIC BIC logLik deviance df.resid
## 204.4 219.0 -95.2 190.4 53
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.4 -0.6 0.1 0.7 1.6
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## system_nr (Intercept) 0.055 0.24
## day 0.002 0.04 -1.00
## Residual 1.182 1.09
## Number of obs: 60, groups: system_nr, 10
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 8.17 0.45 55.73 18.3 <2e-16 ***
## day -0.02 0.02 14.96 -0.7 0.5
## patch_size_symmetrysymmetric 0.28 0.39 13.55 0.7 0.5
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) day
## day -0.786
## ptch_sz_sym -0.437 0.000
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')#Create resid vs fitted
res_vs_fit = filtered_data %>%
mutate(predicted = fitted(reduced_model),
residuals = resid(reduced_model)) %>%
plot_ly(x = ~predicted,
y = ~residuals,
type = "scatter",
mode = "markers",
marker = list(size = 5, color = "#4C78A8"),
text = paste(" System nr: ", filtered_data$system_nr, "<br>",
"Day: ", filtered_data$day, "<br>",
"Meta-ecosystem: ", filtered_data$metaecosystem_type, "<br>",
"Tot Biomass: ", round(filtered_data$total_metaecosystem_bioarea_mm2, digits = 0), "<br>"),
hoverinfo = "text") %>%
plotly::layout(
xaxis = list(title = "Fitted Values"),
yaxis = list(title = "Residuals")
)Click for residual plots
res_vs_fitqqnorm(resid(reduced_model))
model_stats_fixed = compute.model.stats(reduced_model,
null_model,
"mixed_model")
model_stats_fixed %>%
mutate(deltaAIC = round(deltaAIC, digits = 1),
p_value = round(p_value, digits = 3),
R2 = NULL,
evidence = "",
evidence = ifelse(p_value > 0.1,
"none",
evidence),
evidence = ifelse(p_value < 0.1,
"* weak",
evidence),
evidence = ifelse(p_value < 0.05,
"** moderate",
evidence),
evidence = ifelse(p_value < 0.01,
"*** strong",
evidence),
evidence = ifelse(p_value < 0.001,
"**** very strong",
evidence),
p_value = ifelse(p_value < 0.001,
"< 0.001",
p_value)) %>%
print()## deltaAIC p_value evidence
## 1 1.5 0.479 noneresults_table = fill.results.table(
results_table,
response_variable,
metaecosystem_type_i,
model_stats_full,
model_stats_fixed
)SL (meta-ecosystems vs isolated)
metaecosystem_type_i = c("Small-Large meta-ecosystem",
"Small-Large isolated")filtered_data = ds_metaecosystems %>%
filter(time_point >= first_time_point_model,
time_point <= last_time_point_model,
metaecosystem_type %in% metaecosystem_type_i)plot.metaecos.points(filtered_data,
metaecosystem_type_i,
response_variable)
The p-values and ΔAIC of the connection are computed for all possible combinations of the isolated patches, creating a p-value and ΔAIC distributions. We keep as p-value and ΔAIC of the comparison the mean of such distributions.
Click for bootstrapping ΔAIC and p-value
If you see “Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel = ## 1e-15, : unused control arguments ignored”, Ignore it. It just means that you didn’t pass the control argument to the Nelder_Mead optimiser, so it uses the default.
# Compute stats for all patch combinations
isolated_combinations_sets_filtered = isolated_combinations_sets %>%
filter(disturbance == disturbance_global_input,
metaecosystem_type %in% metaecosystem_type_i)
n_sets = isolated_combinations_sets_filtered %>%
pull(set) %>%
max()
iterated_results_table = data.frame(
Response = as.character(NA),
Levels = as.character(NA),
ΔAIC_full = NA,
p_full = NA,
ΔR2_full = NA,
ΔAIC_fix = NA,
p_fix = NA,
ΔR2_fix = NA,
combination_set = NA,
system_nr_isolated_systems = as.character(NA)
) %>%
slice(-1)
for (set_input in 1:n_sets) {
system_nr_isolated_systems_input = isolated_combinations_sets_filtered %>%
filter(
metaecosystem_type %in% metaecosystem_type_i,
connection == "isolated",
set == set_input
) %>%
pull(system_nr)
filtered_data = ds_metaecosystems %>%
filter(
time_point >= first_time_point_model,
time_point <= last_time_point_model,
metaecosystem_type %in% metaecosystem_type_i,
connection == "connected" |
(connection == "isolated" &
system_nr %in% system_nr_isolated_systems_input)
)
null_model = lmer(
get(response_variable) ~
day +
(day | system_nr),
data = filtered_data,
REML = FALSE,
control = lmerControl(optimizer = optimizer_input,
optCtrl = list(method = method_input))
)
full_model = lmer(
get(response_variable) ~
day +
connection +
connection:day +
(day | system_nr),
data = filtered_data,
REML = FALSE,
control = lmerControl(optimizer = optimizer_input,
optCtrl = list(method = method_input))
)
model_stats_full = compute.model.stats(full_model,
null_model,
"mixed_model")
reduced_model = lmer(
get(response_variable) ~
day +
connection +
(day | system_nr),
data = filtered_data,
REML = FALSE,
control = lmerControl(optimizer = optimizer_input,
optCtrl = list(method = method_input))
)
model_stats_fixed = compute.model.stats(reduced_model,
null_model,
"mixed_model")
iterated_results_table = fill.results.table(
iterated_results_table,
response_variable,
metaecosystem_type_i,
model_stats_full,
model_stats_fixed
)
iterated_results_table$set[nrow(iterated_results_table)] = set_input
iterated_results_table$system_nr_isolated_systems[nrow(iterated_results_table)] = paste(system_nr_isolated_systems_input, collapse = " ")
}## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -4.2e+01## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -1.9e+01## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -9.6e+00## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -2.5e+01## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -2.6e+00## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -8.4e+01## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -1.6e+01## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -3.4e+01## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -9.6e+01## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -2.4e+00## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -6.1e-01## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -1.2e+00## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -1.4e+00## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -1.0e-01## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -5.8e+01## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -1.5e+01## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -3.7e+01## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -3.2e+01## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -7.1e+01## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -6.3e+00## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -1.4e+01## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -7.0e+01## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -9.6e+00## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -3.3e+01## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -2.1e-01## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -2.4e+01## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -1.9e+00## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -3.8e+01## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -3.0e+01## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
connection and connection : day)
Click for ΔAIC and p-value distributions
hist(iterated_results_table$ΔAIC_full, main = "Distribution of ΔAIC of the full model.") 
hist(iterated_results_table$p_full, main = "Distribution of p-values of the full model.") 
model_stats_full = data.frame(
deltaAIC = mean(iterated_results_table$ΔAIC_full),
p_value = mean(iterated_results_table$p_full),
R2 = NA
)
model_stats_full %>%
mutate(deltaAIC = round(deltaAIC, digits = 1),
p_value = round(p_value, digits = 3),
R2 = NULL,
evidence = "",
evidence = ifelse(p_value > 0.1,
"none",
evidence),
evidence = ifelse(p_value < 0.1,
"* weak",
evidence),
evidence = ifelse(p_value < 0.05,
"** moderate",
evidence),
evidence = ifelse(p_value < 0.01,
"*** strong",
evidence),
evidence = ifelse(p_value < 0.001,
"**** very strong",
evidence),
p_value = ifelse(p_value < 0.001,
"< 0.001",
p_value)) %>%
print()## deltaAIC p_value evidence
## 1 2.2 0.432 noneClick for which system nr produced which AIC in the full model
iterated_results_table %>%
select(system_nr_isolated_systems,
ΔAIC_full) %>%
arrange(ΔAIC_full)## system_nr_isolated_systems ΔAIC_full
## 1 1002 1009 1013 1016 1025 0.5010946
## 2 1002 1010 1013 1016 1024 0.5570386
## 3 1003 1007 1014 1016 1025 0.6254584
## 4 1005 1007 1013 1016 1024 0.9638950
## 5 1002 1009 1013 1020 1021 0.9759178
## 6 1003 1007 1014 1020 1021 1.1759537
## 7 1002 1006 1013 1020 1024 1.2012104
## 8 1003 1010 1014 1016 1022 1.2401001
## 9 1002 1006 1013 1019 1025 1.2794401
## 10 1002 1010 1013 1019 1021 1.3088651
## 11 1002 1008 1014 1016 1025 1.3159753
## 12 1005 1007 1013 1019 1021 1.3701184
## 13 1005 1009 1013 1016 1022 1.4030246
## 14 1004 1007 1013 1016 1025 1.4352352
## 15 1005 1007 1014 1018 1021 1.4939106
## 16 1002 1010 1014 1018 1021 1.4959565
## 17 1003 1007 1011 1019 1025 1.5059323
## 18 1002 1009 1011 1018 1025 1.5389815
## 19 1002 1006 1014 1018 1025 1.5660723
## 20 1002 1010 1011 1018 1024 1.5673278
## 21 1003 1007 1011 1020 1024 1.5689721
## 22 1001 1007 1013 1019 1025 1.6118521
## 23 1001 1007 1013 1020 1024 1.6227912
## 24 1003 1010 1014 1017 1021 1.6749332
## 25 1003 1006 1012 1020 1024 1.6976609
## 26 1005 1006 1012 1018 1024 1.7255034
## 27 1003 1006 1014 1020 1022 1.7425675
## 28 1003 1006 1014 1017 1025 1.7474833
## 29 1001 1007 1014 1018 1025 1.7954154
## 30 1002 1008 1014 1020 1021 1.7989294
## 31 1005 1008 1014 1016 1022 1.8140579
## 32 1002 1010 1014 1016 1023 1.8221258
## 33 1005 1007 1014 1016 1023 1.8243680
## 34 1004 1007 1013 1020 1021 1.8683582
## 35 1005 1007 1011 1018 1024 1.8980400
## 36 1002 1008 1011 1020 1024 1.9284091
## 37 1005 1009 1013 1017 1021 1.9322277
## 38 1005 1006 1013 1019 1022 1.9405194
## 39 1003 1009 1011 1020 1022 1.9433170
## 40 1003 1010 1011 1019 1022 1.9527565
## 41 1003 1007 1015 1016 1024 1.9637630
## 42 1002 1008 1011 1019 1025 1.9662347
## 43 1003 1010 1012 1016 1024 1.9788621
## 44 1004 1010 1013 1016 1022 1.9891498
## 45 1003 1009 1012 1016 1025 2.0038790
## 46 1005 1006 1014 1018 1022 2.0118944
## 47 1001 1009 1013 1020 1022 2.0178475
## 48 1003 1010 1011 1017 1024 2.0486805
## 49 1003 1009 1011 1017 1025 2.0667909
## 50 1005 1006 1013 1017 1024 2.0817697
## 51 1001 1010 1013 1019 1022 2.0829855
## 52 1005 1008 1012 1016 1024 2.0943513
## 53 1001 1009 1013 1017 1025 2.1275456
## 54 1002 1009 1011 1020 1023 2.1364686
## 55 1001 1010 1013 1017 1024 2.1377212
## 56 1001 1010 1012 1018 1024 2.1601263
## 57 1004 1007 1011 1018 1025 2.1666866
## 58 1005 1009 1011 1018 1022 2.2323686
## 59 1003 1007 1015 1019 1021 2.2363180
## 60 1001 1010 1014 1018 1022 2.2395147
## 61 1002 1010 1011 1019 1023 2.2437344
## 62 1005 1008 1014 1017 1021 2.2640440
## 63 1003 1009 1015 1016 1022 2.2769286
## 64 1003 1006 1012 1019 1025 2.2784983
## 65 1003 1010 1012 1019 1021 2.2792302
## 66 1002 1009 1015 1018 1021 2.3107020
## 67 1002 1006 1014 1020 1023 2.3319116
## 68 1002 1008 1015 1016 1024 2.3450472
## 69 1004 1006 1013 1020 1022 2.3626482
## 70 1003 1009 1012 1020 1021 2.3728213
## 71 1004 1010 1013 1017 1021 2.4161909
## 72 1005 1007 1011 1019 1023 2.4303297
## 73 1004 1006 1013 1017 1025 2.4403362
## 74 1001 1007 1014 1020 1023 2.4594287
## 75 1002 1006 1015 1018 1024 2.4842527
## 76 1001 1008 1014 1020 1022 2.4992415
## 77 1002 1009 1015 1016 1023 2.5079605
## 78 1005 1008 1011 1019 1022 2.5093976
## 79 1001 1008 1014 1017 1025 2.5321129
## 80 1005 1009 1012 1018 1021 2.5754843
## 81 1004 1010 1011 1018 1022 2.6099980
## 82 1003 1006 1015 1019 1022 2.6432113
## 83 1005 1008 1011 1017 1024 2.6492482
## 84 1002 1008 1015 1019 1021 2.6501305
## 85 1001 1008 1012 1020 1024 2.6743575
## 86 1005 1006 1014 1017 1023 2.6758220
## 87 1003 1009 1015 1017 1021 2.6876740
## 88 1001 1007 1015 1018 1024 2.6914272
## 89 1004 1007 1011 1020 1023 2.6995192
## 90 1004 1007 1015 1018 1021 2.7190559
## 91 1005 1008 1012 1019 1021 2.7298626
## 92 1005 1009 1012 1016 1023 2.7483390
## 93 1004 1008 1012 1016 1025 2.7655323
## 94 1003 1006 1015 1017 1024 2.7807335
## 95 1001 1009 1012 1018 1025 2.7868758
## 96 1004 1008 1011 1020 1022 2.7979655
## 97 1005 1009 1011 1017 1023 2.8289796
## 98 1004 1008 1011 1017 1025 2.8402370
## 99 1001 1010 1014 1017 1023 2.8671448
## 100 1004 1007 1015 1016 1023 2.8822776
## 101 1004 1006 1012 1018 1025 2.9060308
## 102 1004 1010 1012 1018 1021 2.9144904
## 103 1001 1009 1015 1018 1022 2.9359005
## 104 1001 1008 1012 1019 1025 2.9392128
## 105 1002 1006 1015 1019 1023 2.9487574
## 106 1004 1008 1015 1016 1022 2.9642506
## 107 1005 1006 1012 1019 1023 2.9961082
## 108 1004 1008 1012 1020 1021 3.0372131
## 109 1004 1010 1012 1016 1023 3.0627098
## 110 1004 1006 1015 1018 1022 3.0712925
## 111 1004 1010 1011 1017 1023 3.0801534
## 112 1001 1007 1015 1019 1023 3.0852168
## 113 1001 1010 1012 1019 1023 3.1583306
## 114 1001 1008 1015 1019 1022 3.1614639
## 115 1001 1009 1012 1020 1023 3.1837762
## 116 1001 1008 1015 1017 1024 3.2462909
## 117 1004 1008 1015 1017 1021 3.2565423
## 118 1004 1006 1012 1020 1023 3.2886902
## 119 1001 1009 1015 1017 1023 3.3609837
## 120 1004 1006 1015 1017 1023 3.4583155
connection)
Click for ΔAIC and p-value dsitributions
hist(iterated_results_table$ΔAIC_fix, main = "Distribution of ΔAIC of the fixed model.") 
hist(iterated_results_table$p_fix, main = "Distribution of p-values of the fixed model.") 
model_stats_fixed = data.frame(
deltaAIC = mean(iterated_results_table$ΔAIC_fix),
p_value = mean(iterated_results_table$p_fix),
R2 = NA
)
model_stats_fixed %>%
mutate(deltaAIC = round(deltaAIC, digits = 1),
p_value = round(p_value, digits = 3),
R2 = NULL,
evidence = "",
evidence = ifelse(p_value > 0.1,
"none",
evidence),
evidence = ifelse(p_value < 0.1,
"* weak",
evidence),
evidence = ifelse(p_value < 0.05,
"** moderate",
evidence),
evidence = ifelse(p_value < 0.01,
"*** strong",
evidence),
evidence = ifelse(p_value < 0.001,
"**** very strong",
evidence),
p_value = ifelse(p_value < 0.001,
"< 0.001",
p_value)) %>%
print()## deltaAIC p_value evidence
## 1 1.8 0.737 noneresults_table = fill.results.table(
results_table,
response_variable,
metaecosystem_type_i,
model_stats_full,
model_stats_fixed
)MM (meta-ecosystems vs isolated)
metaecosystem_type_i = c("Medium-Medium meta-ecosystem",
"Medium-Medium isolated")filtered_data = ds_metaecosystems %>%
filter(time_point >= first_time_point_model,
time_point <= last_time_point_model,
metaecosystem_type %in% metaecosystem_type_i)plot.metaecos.points(filtered_data,
metaecosystem_type_i,
response_variable)
The p-values and ΔAIC of the connection are computed for all possible combinations of the isolated patches, creating a p-value and ΔAIC distributions. We keep as p-value and ΔAIC of the comparison the mean of such distributions.
Click for bootstrapping ΔAIC and p-value
If you see “Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel = ## 1e-15, : unused control arguments ignored”, Ignore it. It just means that you didn’t pass the control argument to the Nelder_Mead optimiser, so it uses the default.
# Compute stats for all patch combinations
isolated_combinations_sets_filtered = isolated_combinations_sets %>%
filter(disturbance == disturbance_global_input,
metaecosystem_type %in% metaecosystem_type_i)
n_sets = isolated_combinations_sets_filtered %>%
pull(set) %>%
max()
iterated_results_table = data.frame(
Response = as.character(NA),
Levels = as.character(NA),
ΔAIC_full = NA,
p_full = NA,
ΔR2_full = NA,
ΔAIC_fix = NA,
p_fix = NA,
ΔR2_fix = NA,
combination_set = NA,
system_nr_isolated_systems = as.character(NA)
) %>%
slice(-1)
for (set_input in 1:n_sets) {
system_nr_isolated_systems_input = isolated_combinations_sets_filtered %>%
filter(
metaecosystem_type %in% metaecosystem_type_i,
connection == "isolated",
set == set_input
) %>%
pull(system_nr)
filtered_data = ds_metaecosystems %>%
filter(
time_point >= first_time_point_model,
time_point <= last_time_point_model,
metaecosystem_type %in% metaecosystem_type_i,
connection == "connected" |
(connection == "isolated" &
system_nr %in% system_nr_isolated_systems_input)
)
null_model = lmer(
get(response_variable) ~
day +
(day | system_nr),
data = filtered_data,
REML = FALSE,
control = lmerControl(optimizer = optimizer_input,
optCtrl = list(method = method_input))
)
full_model = lmer(
get(response_variable) ~
day +
connection +
connection:day +
(day | system_nr),
data = filtered_data,
REML = FALSE,
control = lmerControl(optimizer = optimizer_input,
optCtrl = list(method = method_input))
)
model_stats_full = compute.model.stats(full_model,
null_model,
"mixed_model")
reduced_model = lmer(
get(response_variable) ~
day +
connection +
(day | system_nr),
data = filtered_data,
REML = FALSE,
control = lmerControl(optimizer = optimizer_input,
optCtrl = list(method = method_input))
)
model_stats_fixed = compute.model.stats(reduced_model,
null_model,
"mixed_model")
iterated_results_table = fill.results.table(
iterated_results_table,
response_variable,
metaecosystem_type_i,
model_stats_full,
model_stats_fixed
)
iterated_results_table$set[nrow(iterated_results_table)] = set_input
iterated_results_table$system_nr_isolated_systems[nrow(iterated_results_table)] = paste(system_nr_isolated_systems_input, collapse = " ")
}## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -3.8e-01## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -6.6e+00## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -1.8e+00## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -1.2e+01## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -2.2e+01## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -3.0e+01## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -1.3e+01## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -1.7e+01## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -1.8e+00## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -2.2e+01## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -3.8e-01## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -6.6e+00## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -1.3e+01## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -1.7e+01## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -3.0e+01## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning: Model failed to converge with 1 negative eigenvalue: -1.2e+01## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored## Warning in (function (iprint = 0L, maxfun = 10000L, FtolAbs = 1e-05, FtolRel =
## 1e-15, : unused control arguments ignored
connection and connection : day)
Click for ΔAIC and p-value distributions
hist(iterated_results_table$ΔAIC_full, main = "Distribution of ΔAIC of the full model.") 
hist(iterated_results_table$p_full, main = "Distribution of p-values of the full model.") 
model_stats_full = data.frame(
deltaAIC = mean(iterated_results_table$ΔAIC_full),
p_value = mean(iterated_results_table$p_full),
R2 = NA
)
model_stats_full %>%
mutate(deltaAIC = round(deltaAIC, digits = 1),
p_value = round(p_value, digits = 3),
R2 = NULL,
evidence = "",
evidence = ifelse(p_value > 0.1,
"none",
evidence),
evidence = ifelse(p_value < 0.1,
"* weak",
evidence),
evidence = ifelse(p_value < 0.05,
"** moderate",
evidence),
evidence = ifelse(p_value < 0.01,
"*** strong",
evidence),
evidence = ifelse(p_value < 0.001,
"**** very strong",
evidence),
p_value = ifelse(p_value < 0.001,
"< 0.001",
p_value)) %>%
print()## deltaAIC p_value evidence
## 1 1.3 0.282 noneClick for which system nr produced which AIC in the full model
iterated_results_table %>%
select(system_nr_isolated_systems,
ΔAIC_full) %>%
arrange(ΔAIC_full)## system_nr_isolated_systems ΔAIC_full
## 1 1052 1053 -0.11272141
## 2 1053 1052 -0.11272141
## 3 1048 1052 -0.09352802
## 4 1052 1048 -0.09352802
## 5 1047 1052 0.44975279
## 6 1052 1047 0.44975279
## 7 1048 1054 0.65487503
## 8 1054 1048 0.65487503
## 9 1047 1051 0.83275933
## 10 1051 1047 0.83275933
## 11 1047 1055 1.18971078
## 12 1055 1047 1.18971078
## 13 1048 1050 1.26756805
## 14 1050 1048 1.26756805
## 15 1049 1053 1.54078951
## 16 1053 1049 1.54078951
## 17 1051 1054 1.58837752
## 18 1054 1051 1.58837752
## 19 1046 1053 1.70905621
## 20 1053 1046 1.70905621
## 21 1050 1055 1.72028642
## 22 1055 1050 1.72028642
## 23 1049 1051 1.85003595
## 24 1051 1049 1.85003595
## 25 1046 1055 2.06900784
## 26 1055 1046 2.06900784
## 27 1046 1054 2.36391055
## 28 1054 1046 2.36391055
## 29 1049 1050 2.66198970
## 30 1050 1049 2.66198970
connection)
Click for ΔAIC and p-value dsitributions
hist(iterated_results_table$ΔAIC_fix, main = "Distribution of ΔAIC of the fixed model.") 
hist(iterated_results_table$p_fix, main = "Distribution of p-values of the fixed model.") 
model_stats_fixed = data.frame(
deltaAIC = mean(iterated_results_table$ΔAIC_fix),
p_value = mean(iterated_results_table$p_fix),
R2 = NA
)
model_stats_fixed %>%
mutate(deltaAIC = round(deltaAIC, digits = 1),
p_value = round(p_value, digits = 3),
R2 = NULL,
evidence = "",
evidence = ifelse(p_value > 0.1,
"none",
evidence),
evidence = ifelse(p_value < 0.1,
"* weak",
evidence),
evidence = ifelse(p_value < 0.05,
"** moderate",
evidence),
evidence = ifelse(p_value < 0.01,
"*** strong",
evidence),
evidence = ifelse(p_value < 0.001,
"**** very strong",
evidence),
p_value = ifelse(p_value < 0.001,
"< 0.001",
p_value)) %>%
print()## deltaAIC p_value evidence
## 1 0.4 0.242 noneresults_table = fill.results.table(
results_table,
response_variable,
metaecosystem_type_i,
model_stats_full,
model_stats_fixed
)All patches
patch_type_i = c("Small isolated",
"Medium isolated",
"Large isolated",
"Small connected to small",
"Small connected to large",
"Medium connected to medium",
"Large connected to small",
"Large connected to large")BEF
Richness
ds_patches %>%
filter(species_richness > 0,
patch_type == patch_type_i) %>%
ggplot(aes(x = species_richness,
y = bioarea_mm2_per_ml)) +
geom_point() +
xlim(0, length(protist_species))
for (patch_type in patch_type_i) {
plot.relationship.BEF(patch_type,
"species_richness") %>%
print()
}Shannon
ds_patches %>%
filter(species_richness > 0,
patch_type == patch_type_i) %>%
ggplot(aes(x = shannon,
y = bioarea_mm2_per_ml)) +
geom_point()
for (patch_type in patch_type_i) {
plot.relationship.BEF(patch_type,
"shannon") %>%
print()
}Evenness
ds_patches %>%
filter(species_richness > 0,
patch_type == patch_type_i) %>%
ggplot(aes(x = evenness_pielou,
y = bioarea_mm2_per_ml)) +
geom_point() +
xlim(0, 1)
for (patch_type in patch_type_i) {
plot.relationship.BEF(patch_type,
"evenness_pielou") %>%
print()
}Pop dominance
for(species_input in protist_species_dominance){
print(ds_patches %>%
ggplot(aes(x = get(paste0(species_input)),
y = bioarea_mm2_per_ml)) +
geom_point() +
labs(x = paste0(species_input, "_dominance")))
}## Warning: Removed 1 rows containing missing values (`geom_point()`).
## Warning: Removed 1 rows containing missing values (`geom_point()`).
## Warning: Removed 1 rows containing missing values (`geom_point()`).
## Warning: Removed 1 rows containing missing values (`geom_point()`).
## Warning: Removed 1 rows containing missing values (`geom_point()`).
## Warning: Removed 1 rows containing missing values (`geom_point()`).
## Warning: Removed 1 rows containing missing values (`geom_point()`).
## Warning: Removed 1 rows containing missing values (`geom_point()`).
## Warning: Removed 1 rows containing missing values (`geom_point()`).
## Warning: Removed 1 rows containing missing values (`geom_point()`).
## Warning: Removed 1 rows containing missing values (`geom_point()`).
for (patch_type in patch_type_i) {
plot.relationship.dominance.productivity(patch_type,
protist_species,
protist_species_dominance) %>%
print()
}Isolated patches
patch_type_i = c("Small isolated",
"Medium isolated",
"Large isolated")Biomass
response_variable = "bioarea_mm2_per_ml"plot.patches.points(ds_patches,
patch_type_i,
response_variable)
Click for boxplots instead of means
plot.patches.boxplots(patch_type_i,
response_variable)
save_results_in_table = TRUE
Filter
filtered_data = ds_patches %>%
filter(
metaecosystem == "no",
time_point >= first_time_point_model,
time_point <= last_time_point_model,
!is.na(!!sym(response_variable))
)Plot filtered data
plot.patches.points(filtered_data,
patch_type_i,
response_variable)
Analysis
null_model = lmer(
get(response_variable) ~
day +
(day | culture_ID),
data = filtered_data,
REML = FALSE,
control = lmerControl(optimizer = "Nelder_Mead")
)patch_size_ml and
patch_size_ml : day)
full_model = lmer(
get(response_variable) ~
day +
patch_size_ml +
patch_size_ml : day +
(day | culture_ID),
data = filtered_data,
REML = FALSE,
control = lmerControl(optimizer = "Nelder_Mead")
)Click for model output
print(summary(full_model), digits = 1)## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
## method [lmerModLmerTest]
## Formula: get(response_variable) ~ day + patch_size_ml + patch_size_ml:day +
## (day | culture_ID)
## Data: filtered_data
## Control: lmerControl(optimizer = "Nelder_Mead")
##
## AIC BIC logLik deviance df.resid
## 251.0 271.0 -117.5 235.0 82
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.0 -0.6 0.0 0.6 2.8
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## culture_ID (Intercept) 2e-03 0.04
## day 1e-04 0.01 1.00
## Residual 7e-01 0.86
## Number of obs: 90, groups: culture_ID, 15
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.894 0.534 72.973 7 3e-10 ***
## day -0.183 0.028 39.417 -6 1e-07 ***
## patch_size_ml 0.034 0.021 72.973 2 0.11
## day:patch_size_ml 0.002 0.001 39.417 2 0.07 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) day ptch__
## day -0.900
## patch_sz_ml -0.878 0.790
## dy:ptch_sz_ 0.790 -0.878 -0.900
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')#Create resid vs fitted
res_vs_fit = filtered_data %>%
mutate(predicted = fitted(full_model),
residuals = resid(full_model)) %>%
plot_ly(x = ~predicted,
y = ~residuals,
type = "scatter",
mode = "markers",
marker = list(size = 5, color = "#4C78A8"),
text = paste(" ID: ", filtered_data$culture_ID, "<br>",
"Day: ", filtered_data$day, "<br>",
"Patch Type: ", filtered_data$patch_type, "<br>",
"Biomass density: ", round(filtered_data$bioarea_mm2_per_ml, digits = 2), "<br>",
"Species richness: ", filtered_data$species_richness, "<br>"),
hoverinfo = "text") %>%
plotly::layout(
title = "Residuals vs. Fitted Values",
xaxis = list(title = "Fitted Values"),
yaxis = list(title = "Residuals")
)Click for residual plots
res_vs_fitqqnorm(resid(full_model))
model_stats_full = compute.model.stats(full_model,
null_model,
"mixed_model")
model_stats_full %>%
mutate(deltaAIC = round(deltaAIC, digits = 1),
p_value = round(p_value, digits = 3),
R2 = NULL,
evidence = "",
evidence = ifelse(p_value > 0.1,
"none",
evidence),
evidence = ifelse(p_value < 0.1,
"* weak",
evidence),
evidence = ifelse(p_value < 0.05,
"** moderate",
evidence),
evidence = ifelse(p_value < 0.01,
"*** strong",
evidence),
evidence = ifelse(p_value < 0.001,
"**** very strong",
evidence),
p_value = ifelse(p_value < 0.001,
"< 0.001",
p_value)) %>%
print()## deltaAIC p_value evidence
## 1 -20.2 < 0.001 **** very strong
patch_size_ml)
reduced_model = lmer(
get(response_variable) ~
day +
patch_size_ml +
(day | culture_ID),
data = filtered_data,
REML = FALSE,
control = lmerControl(optimizer = "Nelder_Mead")
)Click for model output
print(summary(reduced_model), digits = 1)## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
## method [lmerModLmerTest]
## Formula: get(response_variable) ~ day + patch_size_ml + (day | culture_ID)
## Data: filtered_data
## Control: lmerControl(optimizer = "Nelder_Mead")
##
## AIC BIC logLik deviance df.resid
## 252.2 269.7 -119.1 238.2 83
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.0 -0.6 0.0 0.6 3.1
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## culture_ID (Intercept) 7e-02 0.26
## day 5e-04 0.02 -0.80
## Residual 8e-01 0.87
## Number of obs: 90, groups: culture_ID, 15
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.130 0.335 23.327 9 2e-09 ***
## day -0.137 0.015 15.000 -9 1e-07 ***
## patch_size_ml 0.068 0.009 15.000 8 2e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) day
## day -0.723
## patch_sz_ml -0.606 0.000#Create resid vs fitted
res_vs_fit = filtered_data %>%
mutate(predicted = fitted(reduced_model),
residuals = resid(reduced_model)) %>%
plot_ly(x = ~predicted,
y = ~residuals,
type = "scatter",
mode = "markers",
marker = list(size = 5, color = "#4C78A8"),
text = paste(" ID: ", filtered_data$culture_ID, "<br>",
"Day: ", filtered_data$day, "<br>",
"Patch Type: ", filtered_data$patch_type, "<br>",
"Biomass density: ", round(filtered_data$bioarea_mm2_per_ml, digits = 2), "<br>",
"Species richness: ", filtered_data$species_richness, "<br>"),
hoverinfo = "text") %>%
plotly::layout(
title = "Residuals vs. Fitted Values",
xaxis = list(title = "Fitted Values"),
yaxis = list(title = "Residuals")
)Click for residual plots
res_vs_fitqqnorm(resid(reduced_model))
model_stats_fixed = compute.model.stats(reduced_model,
null_model,
"mixed_model")
model_stats_fixed %>%
mutate(deltaAIC = round(deltaAIC, digits = 1),
p_value = round(p_value, digits = 3),
R2 = NULL,
evidence = "",
evidence = ifelse(p_value > 0.1,
"none",
evidence),
evidence = ifelse(p_value < 0.1,
"* weak",
evidence),
evidence = ifelse(p_value < 0.05,
"** moderate",
evidence),
evidence = ifelse(p_value < 0.01,
"*** strong",
evidence),
evidence = ifelse(p_value < 0.001,
"**** very strong",
evidence),
p_value = ifelse(p_value < 0.001,
"< 0.001",
p_value)) %>%
print()## deltaAIC p_value evidence
## 1 -19 < 0.001 **** very strongresults_table = fill.results.table(results_table,
response_variable,
patch_type_i,
model_stats_full,
model_stats_fixed)Shannon
response_variable = "shannon"plot.patches.points(ds_patches,
patch_type_i,
response_variable)
Click for boxplots instead of means
plot.patches.boxplots(patch_type_i,
response_variable)## Warning: Removed 1 rows containing non-finite values (`stat_boxplot()`).
save_results_in_table = TRUE
Filter
filtered_data = ds_patches %>%
filter(
metaecosystem == "no",
time_point >= first_time_point_model,
time_point <= last_time_point_model,
!is.na(!!sym(response_variable))
)Plot filtered data
plot.patches.points(filtered_data,
patch_type_i,
response_variable)
Analysis
null_model = lmer(
get(response_variable) ~
day +
(day | culture_ID),
data = filtered_data,
REML = FALSE,
control = lmerControl(optimizer = "Nelder_Mead")
)patch_size_ml and
patch_size_ml : day)
full_model = lmer(
get(response_variable) ~
day +
patch_size_ml +
patch_size_ml : day +
(day | culture_ID),
data = filtered_data,
REML = FALSE,
control = lmerControl(optimizer = "Nelder_Mead")
)Click for model output
print(summary(full_model), digits = 1)## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
## method [lmerModLmerTest]
## Formula: get(response_variable) ~ day + patch_size_ml + patch_size_ml:day +
## (day | culture_ID)
## Data: filtered_data
## Control: lmerControl(optimizer = "Nelder_Mead")
##
## AIC BIC logLik deviance df.resid
## 65.7 85.6 -24.8 49.7 81
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.65 -0.63 0.03 0.72 2.46
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## culture_ID (Intercept) 9e-02 0.30
## day 4e-04 0.02 -1.00
## Residual 9e-02 0.29
## Number of obs: 89, groups: culture_ID, 15
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2e+00 2e-01 2e+01 6.3 7e-06 ***
## day -4e-02 1e-02 2e+01 -2.8 0.01 *
## patch_size_ml 2e-03 1e-02 2e+01 0.2 0.88
## day:patch_size_ml 1e-03 6e-04 2e+01 1.8 0.10 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) day ptch__
## day -0.960
## patch_sz_ml -0.880 0.844
## dy:ptch_sz_ 0.846 -0.881 -0.960
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')#Create resid vs fitted
res_vs_fit = filtered_data %>%
mutate(predicted = fitted(full_model),
residuals = resid(full_model)) %>%
plot_ly(x = ~predicted,
y = ~residuals,
type = "scatter",
mode = "markers",
marker = list(size = 5, color = "#4C78A8"),
text = paste(" ID: ", filtered_data$culture_ID, "<br>",
"Day: ", filtered_data$day, "<br>",
"Patch Type: ", filtered_data$patch_type, "<br>",
"Biomass density: ", round(filtered_data$bioarea_mm2_per_ml, digits = 2), "<br>",
"Species richness: ", filtered_data$species_richness, "<br>"),
hoverinfo = "text") %>%
plotly::layout(
title = "Residuals vs. Fitted Values",
xaxis = list(title = "Fitted Values"),
yaxis = list(title = "Residuals")
)Click for residual plots
res_vs_fitqqnorm(resid(full_model))
model_stats_full = compute.model.stats(full_model,
null_model,
"mixed_model")
model_stats_full %>%
mutate(deltaAIC = round(deltaAIC, digits = 1),
p_value = round(p_value, digits = 3),
R2 = NULL,
evidence = "",
evidence = ifelse(p_value > 0.1,
"none",
evidence),
evidence = ifelse(p_value < 0.1,
"* weak",
evidence),
evidence = ifelse(p_value < 0.05,
"** moderate",
evidence),
evidence = ifelse(p_value < 0.01,
"*** strong",
evidence),
evidence = ifelse(p_value < 0.001,
"**** very strong",
evidence),
p_value = ifelse(p_value < 0.001,
"< 0.001",
p_value)) %>%
print()## deltaAIC p_value evidence
## 1 -18.6 < 0.001 **** very strong
patch_size_ml)
reduced_model = lmer(
get(response_variable) ~
day +
patch_size_ml +
(day | culture_ID),
data = filtered_data,
REML = FALSE,
control = lmerControl(optimizer = "Nelder_Mead")
)Click for model output
print(summary(reduced_model), digits = 1)## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
## method [lmerModLmerTest]
## Formula: get(response_variable) ~ day + patch_size_ml + (day | culture_ID)
## Data: filtered_data
## Control: lmerControl(optimizer = "Nelder_Mead")
##
## AIC BIC logLik deviance df.resid
## 66.5 83.9 -26.3 52.5 82
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.72 -0.57 0.03 0.66 2.49
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## culture_ID (Intercept) 1e-01 0.36
## day 6e-04 0.02 -1.00
## Residual 9e-02 0.30
## Number of obs: 89, groups: culture_ID, 15
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.196 0.142 24.725 8 9e-09 ***
## day -0.018 0.008 14.809 -2 0.03 *
## patch_size_ml 0.018 0.003 53.014 7 2e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) day
## day -0.871
## patch_sz_ml -0.423 -0.010
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')#Create resid vs fitted
res_vs_fit = filtered_data %>%
mutate(predicted = fitted(reduced_model),
residuals = resid(reduced_model)) %>%
plot_ly(x = ~predicted,
y = ~residuals,
type = "scatter",
mode = "markers",
marker = list(size = 5, color = "#4C78A8"),
text = paste(" ID: ", filtered_data$culture_ID, "<br>",
"Day: ", filtered_data$day, "<br>",
"Patch Type: ", filtered_data$patch_type, "<br>",
"Biomass density: ", round(filtered_data$bioarea_mm2_per_ml, digits = 2), "<br>",
"Species richness: ", filtered_data$species_richness, "<br>"),
hoverinfo = "text") %>%
plotly::layout(
title = "Residuals vs. Fitted Values",
xaxis = list(title = "Fitted Values"),
yaxis = list(title = "Residuals")
)Click for residual plots
res_vs_fitqqnorm(resid(reduced_model))
model_stats_fixed = compute.model.stats(reduced_model,
null_model,
"mixed_model")
model_stats_fixed %>%
mutate(deltaAIC = round(deltaAIC, digits = 1),
p_value = round(p_value, digits = 3),
R2 = NULL,
evidence = "",
evidence = ifelse(p_value > 0.1,
"none",
evidence),
evidence = ifelse(p_value < 0.1,
"* weak",
evidence),
evidence = ifelse(p_value < 0.05,
"** moderate",
evidence),
evidence = ifelse(p_value < 0.01,
"*** strong",
evidence),
evidence = ifelse(p_value < 0.001,
"**** very strong",
evidence),
p_value = ifelse(p_value < 0.001,
"< 0.001",
p_value)) %>%
print()## deltaAIC p_value evidence
## 1 -17.7 < 0.001 **** very strongresults_table = fill.results.table(results_table,
response_variable,
patch_type_i,
model_stats_full,
model_stats_fixed)Richness
response_variable = "species_richness"plot.patches.points(ds_patches,
patch_type_i,
response_variable)
Click for boxplots instead of means
plot.patches.boxplots(patch_type_i,
response_variable)
save_results_in_table = TRUE
Filter
filtered_data = ds_patches %>%
filter(
metaecosystem == "no",
time_point >= first_time_point_model,
time_point <= last_time_point_model,
!is.na(!!sym(response_variable))
)Plot filtered data
plot.patches.points(filtered_data,
patch_type_i,
response_variable)
Analysis
null_model = lmer(
get(response_variable) ~
day +
(day | culture_ID),
data = filtered_data,
REML = FALSE,
control = lmerControl(optimizer = "Nelder_Mead")
)patch_size_ml and
patch_size_ml : day)
full_model = lmer(
get(response_variable) ~
day +
patch_size_ml +
patch_size_ml : day +
(day | culture_ID),
data = filtered_data,
REML = FALSE,
control = lmerControl(optimizer = "Nelder_Mead")
)Click for model output
print(summary(full_model), digits = 1)## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
## method [lmerModLmerTest]
## Formula: get(response_variable) ~ day + patch_size_ml + patch_size_ml:day +
## (day | culture_ID)
## Data: filtered_data
## Control: lmerControl(optimizer = "Nelder_Mead")
##
## AIC BIC logLik deviance df.resid
## 351.4 371.4 -167.7 335.4 82
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.1 -0.8 -0.1 0.5 2.2
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## culture_ID (Intercept) 2e-02 0.13
## day 1e-04 0.01 -1.00
## Residual 2e+00 1.56
## Number of obs: 90, groups: culture_ID, 15
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 8.290 0.969 80.981 8.6 6e-13 ***
## day -0.294 0.051 44.221 -5.8 6e-07 ***
## patch_size_ml -0.026 0.038 80.981 -0.7 0.5
## day:patch_size_ml 0.008 0.002 44.221 3.9 3e-04 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) day ptch__
## day -0.934
## patch_sz_ml -0.878 0.821
## dy:ptch_sz_ 0.821 -0.878 -0.934
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')#Create resid vs fitted
res_vs_fit = filtered_data %>%
mutate(predicted = fitted(full_model),
residuals = resid(full_model)) %>%
plot_ly(x = ~predicted,
y = ~residuals,
type = "scatter",
mode = "markers",
marker = list(size = 5, color = "#4C78A8"),
text = paste(" ID: ", filtered_data$culture_ID, "<br>",
"Day: ", filtered_data$day, "<br>",
"Patch Type: ", filtered_data$patch_type, "<br>",
"Biomass density: ", round(filtered_data$bioarea_mm2_per_ml, digits = 2), "<br>",
"Species richness: ", filtered_data$species_richness, "<br>"),
hoverinfo = "text") %>%
plotly::layout(
title = "Residuals vs. Fitted Values",
xaxis = list(title = "Fitted Values"),
yaxis = list(title = "Residuals")
)Click for residual plots
res_vs_fitqqnorm(resid(full_model))
model_stats_full = compute.model.stats(full_model,
null_model,
"mixed_model")
model_stats_full %>%
mutate(deltaAIC = round(deltaAIC, digits = 1),
p_value = round(p_value, digits = 3),
R2 = NULL,
evidence = "",
evidence = ifelse(p_value > 0.1,
"none",
evidence),
evidence = ifelse(p_value < 0.1,
"* weak",
evidence),
evidence = ifelse(p_value < 0.05,
"** moderate",
evidence),
evidence = ifelse(p_value < 0.01,
"*** strong",
evidence),
evidence = ifelse(p_value < 0.001,
"**** very strong",
evidence),
p_value = ifelse(p_value < 0.001,
"< 0.001",
p_value)) %>%
print()## deltaAIC p_value evidence
## 1 -25 < 0.001 **** very strong
patch_size_ml)
reduced_model = lmer(
get(response_variable) ~
day +
patch_size_ml +
(day | culture_ID),
data = filtered_data,
REML = FALSE,
control = lmerControl(optimizer = "Nelder_Mead")
)Click for model output
print(summary(reduced_model), digits = 1)## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
## method [lmerModLmerTest]
## Formula: get(response_variable) ~ day + patch_size_ml + (day | culture_ID)
## Data: filtered_data
## Control: lmerControl(optimizer = "Nelder_Mead")
##
## AIC BIC logLik deviance df.resid
## 360.9 378.4 -173.4 346.9 83
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.78 -0.75 -0.04 0.57 1.84
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## culture_ID (Intercept) 1.804 1.34
## day 0.007 0.09 -1.00
## Residual 2.478 1.57
## Number of obs: 90, groups: culture_ID, 15
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 5.35 0.66 29.20 8 6e-09 ***
## day -0.12 0.03 15.34 -4 0.002 **
## patch_size_ml 0.11 0.01 52.47 8 6e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) day
## day -0.843
## patch_sz_ml -0.473 0.000
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')#Create resid vs fitted
res_vs_fit = filtered_data %>%
mutate(predicted = fitted(reduced_model),
residuals = resid(reduced_model)) %>%
plot_ly(x = ~predicted,
y = ~residuals,
type = "scatter",
mode = "markers",
marker = list(size = 5, color = "#4C78A8"),
text = paste(" ID: ", filtered_data$culture_ID, "<br>",
"Day: ", filtered_data$day, "<br>",
"Patch Type: ", filtered_data$patch_type, "<br>",
"Biomass density: ", round(filtered_data$bioarea_mm2_per_ml, digits = 2), "<br>",
"Species richness: ", filtered_data$species_richness, "<br>"),
hoverinfo = "text") %>%
plotly::layout(
title = "Residuals vs. Fitted Values",
xaxis = list(title = "Fitted Values"),
yaxis = list(title = "Residuals")
)Click for residual plots
res_vs_fitqqnorm(resid(reduced_model))
model_stats_fixed = compute.model.stats(reduced_model,
null_model,
"mixed_model")
model_stats_fixed %>%
mutate(deltaAIC = round(deltaAIC, digits = 1),
p_value = round(p_value, digits = 3),
R2 = NULL,
evidence = "",
evidence = ifelse(p_value > 0.1,
"none",
evidence),
evidence = ifelse(p_value < 0.1,
"* weak",
evidence),
evidence = ifelse(p_value < 0.05,
"** moderate",
evidence),
evidence = ifelse(p_value < 0.01,
"*** strong",
evidence),
evidence = ifelse(p_value < 0.001,
"**** very strong",
evidence),
p_value = ifelse(p_value < 0.001,
"< 0.001",
p_value)) %>%
print()## deltaAIC p_value evidence
## 1 -15.6 < 0.001 **** very strongresults_table = fill.results.table(results_table,
response_variable,
patch_type_i,
model_stats_full,
model_stats_fixed)Evenness
response_variable = "evenness_pielou"plot.patches.points(ds_patches,
patch_type_i,
response_variable)
Click for boxplots instead of means
plot.patches.boxplots(patch_type_i,
response_variable)## Warning: Removed 3 rows containing non-finite values (`stat_boxplot()`).
save_results_in_table = TRUE
Filter
filtered_data = ds_patches %>%
filter(
metaecosystem == "no",
time_point >= first_time_point_model,
time_point <= last_time_point_model,
!is.na(!!sym(response_variable))
)Plot filtered data
plot.patches.points(filtered_data,
patch_type_i,
response_variable)
Analysis
null_model = lmer(
get(response_variable) ~
day +
(day | culture_ID),
data = filtered_data,
REML = FALSE,
control = lmerControl(optimizer = "Nelder_Mead")
)patch_size_ml and
patch_size_ml : day)
full_model = lmer(
get(response_variable) ~
day +
patch_size_ml +
patch_size_ml : day +
(day | culture_ID),
data = filtered_data,
REML = FALSE,
control = lmerControl(optimizer = "Nelder_Mead")
)Click for model output
print(summary(full_model), digits = 1)## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
## method [lmerModLmerTest]
## Formula: get(response_variable) ~ day + patch_size_ml + patch_size_ml:day +
## (day | culture_ID)
## Data: filtered_data
## Control: lmerControl(optimizer = "Nelder_Mead")
##
## AIC BIC logLik deviance df.resid
## -82.1 -62.4 49.1 -98.1 79
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.4 -0.5 0.0 0.6 2.8
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## culture_ID (Intercept) 1e-02 0.121
## day 7e-05 0.009 -1.00
## Residual 2e-02 0.127
## Number of obs: 87, groups: culture_ID, 15
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 8e-01 1e-01 2e+01 7.2 7e-07 ***
## day 2e-03 6e-03 2e+01 0.4 0.7
## patch_size_ml 2e-03 4e-03 2e+01 0.5 0.6
## day:patch_size_ml -1e-04 2e-04 2e+01 -0.5 0.6
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) day ptch__
## day -0.954
## patch_sz_ml -0.882 0.840
## dy:ptch_sz_ 0.845 -0.884 -0.954
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')#Create resid vs fitted
res_vs_fit = filtered_data %>%
mutate(predicted = fitted(full_model),
residuals = resid(full_model)) %>%
plot_ly(x = ~predicted,
y = ~residuals,
type = "scatter",
mode = "markers",
marker = list(size = 5, color = "#4C78A8"),
text = paste(" ID: ", filtered_data$culture_ID, "<br>",
"Day: ", filtered_data$day, "<br>",
"Patch Type: ", filtered_data$patch_type, "<br>",
"Biomass density: ", round(filtered_data$bioarea_mm2_per_ml, digits = 2), "<br>",
"Species richness: ", filtered_data$species_richness, "<br>"),
hoverinfo = "text") %>%
plotly::layout(
title = "Residuals vs. Fitted Values",
xaxis = list(title = "Fitted Values"),
yaxis = list(title = "Residuals")
)Click for residual plots
res_vs_fitqqnorm(resid(full_model))
model_stats_full = compute.model.stats(full_model,
null_model,
"mixed_model")
model_stats_full %>%
mutate(deltaAIC = round(deltaAIC, digits = 1),
p_value = round(p_value, digits = 3),
R2 = NULL,
evidence = "",
evidence = ifelse(p_value > 0.1,
"none",
evidence),
evidence = ifelse(p_value < 0.1,
"* weak",
evidence),
evidence = ifelse(p_value < 0.05,
"** moderate",
evidence),
evidence = ifelse(p_value < 0.01,
"*** strong",
evidence),
evidence = ifelse(p_value < 0.001,
"**** very strong",
evidence),
p_value = ifelse(p_value < 0.001,
"< 0.001",
p_value)) %>%
print()## deltaAIC p_value evidence
## 1 3.8 0.893 none
patch_size_ml)
reduced_model = lmer(
get(response_variable) ~
day +
patch_size_ml +
(day | culture_ID),
data = filtered_data,
REML = FALSE,
control = lmerControl(optimizer = "Nelder_Mead")
)Click for model output
print(summary(reduced_model), digits = 1)## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
## method [lmerModLmerTest]
## Formula: get(response_variable) ~ day + patch_size_ml + (day | culture_ID)
## Data: filtered_data
## Control: lmerControl(optimizer = "Nelder_Mead")
##
## AIC BIC logLik deviance df.resid
## -83.9 -66.6 48.9 -97.9 80
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.4 -0.5 0.0 0.6 2.8
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## culture_ID (Intercept) 1e-02 0.122
## day 7e-05 0.009 -1.00
## Residual 2e-02 0.127
## Number of obs: 87, groups: culture_ID, 15
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 8e-01 6e-02 3e+01 14.1 2e-15 ***
## day -3e-04 3e-03 2e+01 -0.1 0.9
## patch_size_ml 8e-05 1e-03 5e+01 0.1 0.9
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) day
## day -0.830
## patch_sz_ml -0.469 -0.027
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')#Create resid vs fitted
res_vs_fit = filtered_data %>%
mutate(predicted = fitted(reduced_model),
residuals = resid(reduced_model)) %>%
plot_ly(x = ~predicted,
y = ~residuals,
type = "scatter",
mode = "markers",
marker = list(size = 5, color = "#4C78A8"),
text = paste(" ID: ", filtered_data$culture_ID, "<br>",
"Day: ", filtered_data$day, "<br>",
"Patch Type: ", filtered_data$patch_type, "<br>",
"Biomass density: ", round(filtered_data$bioarea_mm2_per_ml, digits = 2), "<br>",
"Species richness: ", filtered_data$species_richness, "<br>"),
hoverinfo = "text") %>%
plotly::layout(
title = "Residuals vs. Fitted Values",
xaxis = list(title = "Fitted Values"),
yaxis = list(title = "Residuals")
)Click for residual plots
res_vs_fitqqnorm(resid(reduced_model))
model_stats_fixed = compute.model.stats(reduced_model,
null_model,
"mixed_model")
model_stats_fixed %>%
mutate(deltaAIC = round(deltaAIC, digits = 1),
p_value = round(p_value, digits = 3),
R2 = NULL,
evidence = "",
evidence = ifelse(p_value > 0.1,
"none",
evidence),
evidence = ifelse(p_value < 0.1,
"* weak",
evidence),
evidence = ifelse(p_value < 0.05,
"** moderate",
evidence),
evidence = ifelse(p_value < 0.01,
"*** strong",
evidence),
evidence = ifelse(p_value < 0.001,
"**** very strong",
evidence),
p_value = ifelse(p_value < 0.001,
"< 0.001",
p_value)) %>%
print()## deltaAIC p_value evidence
## 1 2 0.947 noneresults_table = fill.results.table(results_table,
response_variable,
patch_type_i,
model_stats_full,
model_stats_fixed)Median Size
response_variable = "median_body_area_µm2"plot.patches.points(ds_patches,
patch_type_i,
response_variable)
Click for boxplots instead of means
plot.patches.boxplots(patch_type_i,
response_variable)## Warning: Removed 1 rows containing non-finite values (`stat_boxplot()`).
save_results_in_table = TRUE
Filter
filtered_data = ds_patches %>%
filter(
metaecosystem == "no",
time_point >= first_time_point_model,
time_point <= last_time_point_model,
!is.na(!!sym(response_variable))
)Plot filtered data
plot.patches.points(filtered_data,
patch_type_i,
response_variable)
Analysis
null_model = lmer(
get(response_variable) ~
day +
(day | culture_ID),
data = filtered_data,
REML = FALSE,
control = lmerControl(optimizer = "Nelder_Mead")
)patch_size_ml and
patch_size_ml : day)
full_model = lmer(
get(response_variable) ~
day +
patch_size_ml +
patch_size_ml : day +
(day | culture_ID),
data = filtered_data,
REML = FALSE,
control = lmerControl(optimizer = "Nelder_Mead")
)Click for model output
print(summary(full_model), digits = 1)## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
## method [lmerModLmerTest]
## Formula: get(response_variable) ~ day + patch_size_ml + patch_size_ml:day +
## (day | culture_ID)
## Data: filtered_data
## Control: lmerControl(optimizer = "Nelder_Mead")
##
## AIC BIC logLik deviance df.resid
## 1365.8 1385.7 -674.9 1349.8 81
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.93 -0.59 -0.05 0.61 2.21
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## culture_ID (Intercept) 2e+03 39
## day 2e+01 4 -1.00
## Residual 2e+05 473
## Number of obs: 89, groups: culture_ID, 15
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3130.1 299.2 77.6 10.5 <2e-16 ***
## day 28.1 15.9 51.0 1.8 0.08 .
## patch_size_ml 3.7 11.6 77.3 0.3 0.75
## day:patch_size_ml -0.3 0.6 50.8 -0.4 0.67
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) day ptch__
## day -0.933
## patch_sz_ml -0.881 0.821
## dy:ptch_sz_ 0.825 -0.883 -0.933
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')#Create resid vs fitted
res_vs_fit = filtered_data %>%
mutate(predicted = fitted(full_model),
residuals = resid(full_model)) %>%
plot_ly(x = ~predicted,
y = ~residuals,
type = "scatter",
mode = "markers",
marker = list(size = 5, color = "#4C78A8"),
text = paste(" ID: ", filtered_data$culture_ID, "<br>",
"Day: ", filtered_data$day, "<br>",
"Patch Type: ", filtered_data$patch_type, "<br>",
"Biomass density: ", round(filtered_data$bioarea_mm2_per_ml, digits = 2), "<br>",
"Species richness: ", filtered_data$species_richness, "<br>"),
hoverinfo = "text") %>%
plotly::layout(
title = "Residuals vs. Fitted Values",
xaxis = list(title = "Fitted Values"),
yaxis = list(title = "Residuals")
)Click for residual plots
res_vs_fitqqnorm(resid(full_model))
model_stats_full = compute.model.stats(full_model,
null_model,
"mixed_model")
model_stats_full %>%
mutate(deltaAIC = round(deltaAIC, digits = 1),
p_value = round(p_value, digits = 3),
R2 = NULL,
evidence = "",
evidence = ifelse(p_value > 0.1,
"none",
evidence),
evidence = ifelse(p_value < 0.1,
"* weak",
evidence),
evidence = ifelse(p_value < 0.05,
"** moderate",
evidence),
evidence = ifelse(p_value < 0.01,
"*** strong",
evidence),
evidence = ifelse(p_value < 0.001,
"**** very strong",
evidence),
p_value = ifelse(p_value < 0.001,
"< 0.001",
p_value)) %>%
print()## deltaAIC p_value evidence
## 1 3.8 0.892 none
patch_size_ml)
reduced_model = lmer(
get(response_variable) ~
day +
patch_size_ml +
(day | culture_ID),
data = filtered_data,
REML = FALSE,
control = lmerControl(optimizer = "Nelder_Mead")
)Click for model output
print(summary(reduced_model), digits = 1)## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
## method [lmerModLmerTest]
## Formula: get(response_variable) ~ day + patch_size_ml + (day | culture_ID)
## Data: filtered_data
## Control: lmerControl(optimizer = "Nelder_Mead")
##
## AIC BIC logLik deviance df.resid
## 1363.9 1381.4 -675.0 1349.9 82
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.90 -0.54 -0.05 0.66 2.26
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## culture_ID (Intercept) 2e+03 47
## day 2e+01 5 -1.00
## Residual 2e+05 473
## Number of obs: 89, groups: culture_ID, 15
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3235.5 169.3 82.9 19.1 <2e-16 ***
## day 22.1 7.5 45.9 2.9 0.005 **
## patch_size_ml -0.9 4.2 33.6 -0.2 0.829
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) day
## day -0.771
## patch_sz_ml -0.546 -0.019
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')#Create resid vs fitted
res_vs_fit = filtered_data %>%
mutate(predicted = fitted(reduced_model),
residuals = resid(reduced_model)) %>%
plot_ly(x = ~predicted,
y = ~residuals,
type = "scatter",
mode = "markers",
marker = list(size = 5, color = "#4C78A8"),
text = paste(" ID: ", filtered_data$culture_ID, "<br>",
"Day: ", filtered_data$day, "<br>",
"Patch Type: ", filtered_data$patch_type, "<br>",
"Biomass density: ", round(filtered_data$bioarea_mm2_per_ml, digits = 2), "<br>",
"Species richness: ", filtered_data$species_richness, "<br>"),
hoverinfo = "text") %>%
plotly::layout(
title = "Residuals vs. Fitted Values",
xaxis = list(title = "Fitted Values"),
yaxis = list(title = "Residuals")
)Click for residual plots
res_vs_fitqqnorm(resid(reduced_model))
model_stats_fixed = compute.model.stats(reduced_model,
null_model,
"mixed_model")
model_stats_fixed %>%
mutate(deltaAIC = round(deltaAIC, digits = 1),
p_value = round(p_value, digits = 3),
R2 = NULL,
evidence = "",
evidence = ifelse(p_value > 0.1,
"none",
evidence),
evidence = ifelse(p_value < 0.1,
"* weak",
evidence),
evidence = ifelse(p_value < 0.05,
"** moderate",
evidence),
evidence = ifelse(p_value < 0.01,
"*** strong",
evidence),
evidence = ifelse(p_value < 0.001,
"**** very strong",
evidence),
p_value = ifelse(p_value < 0.001,
"< 0.001",
p_value)) %>%
print()## deltaAIC p_value evidence
## 1 2 0.829 noneresults_table = fill.results.table(results_table,
response_variable,
patch_type_i,
model_stats_full,
model_stats_fixed)Populations
for(patch_type_i in patch_type_i){
print(patch_type_i)
p = ds_patches %>%
filter(patch_type == patch_type_i) %>%
group_by(day) %>%
summarise(
Ble = mean(Ble_indiv_per_ml_dominance, na.rm = TRUE),
# Ble_se = sd(Ble_indiv_per_ml_dominance, na.rm = TRUE) / sqrt(n()),
# Ble_ci = qt(c(0.025, 0.975), df = n() - 1) * Ble_se,
Cep = mean(Cep_indiv_per_ml_dominance, na.rm = TRUE),
Col = mean(Col_indiv_per_ml_dominance, na.rm = TRUE),
Eug = mean(Eug_indiv_per_ml_dominance, na.rm = TRUE),
Eup = mean(Eup_indiv_per_ml_dominance, na.rm = TRUE),
Lox = mean(Lox_indiv_per_ml_dominance, na.rm = TRUE),
Pau = mean(Pau_indiv_per_ml_dominance, na.rm = TRUE),
Pca = mean(Pca_indiv_per_ml_dominance, na.rm = TRUE),
Spi = mean(Spi_indiv_per_ml_dominance, na.rm = TRUE),
Spi_te = mean(Spi_te_indiv_per_ml_dominance, na.rm = TRUE),
Tet = mean(Tet_indiv_per_ml_dominance, na.rm = TRUE)
) %>%
pivot_longer(Ble:Tet, names_to = "species", values_to = "species_indiv_per_ml") %>%
ggplot(aes(
x = day,
y = species_indiv_per_ml,
group = interaction(day, species),
color = species
)) +
geom_point(position = position_dodge(dodging),
size = treatment_points_size) +
geom_line(
aes(group = species),
position = position_dodge(dodging),
linewidth = treatment_lines_linewidth) +
labs(x = axis_names %>%
filter(variable == "day") %>%
pull(axis_name),
y = axis_names %>%
filter(variable == "dominance") %>%
pull(axis_name)) +
coord_cartesian(ylim = c(0, 100))
print(p)
}## [1] "Small isolated"
## [1] "Medium isolated"
## [1] "Large isolated"
S_L vs S_S
patch_type_i = c("Small connected to large",
"Small connected to small")Biomass
response_variable = "bioarea_mm2_per_ml"filtered_data = ds_patches %>%
filter(time_point >= first_time_point_model,
time_point <= last_time_point_model,
patch_type %in% patch_type_i,
!is.na(!!sym(response_variable)))plot.patches.points(filtered_data,
patch_type_i,
response_variable) 
null_model = lmer(
get(response_variable) ~
day +
(day | culture_ID),
data = filtered_data,
REML = FALSE,
control = lmerControl(optimizer = "Nelder_Mead")
)patch_type and
patch_type : day)
full_model = lmer(
get(response_variable) ~
day +
patch_type +
patch_type : day +
(day | culture_ID),
data = filtered_data,
REML = FALSE,
control = lmerControl(optimizer = "Nelder_Mead")
)Click for model output
print(summary(full_model), digits = 1)## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
## method [lmerModLmerTest]
## Formula: get(response_variable) ~ day + patch_type + patch_type:day +
## (day | culture_ID)
## Data: filtered_data
## Control: lmerControl(optimizer = "Nelder_Mead")
##
## AIC BIC logLik deviance df.resid
## 147.6 167.6 -65.8 131.6 82
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.99 -0.75 0.07 0.67 2.18
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## culture_ID (Intercept) 2e-01 0.50
## day 4e-04 0.02 -1.00
## Residual 2e-01 0.47
## Number of obs: 90, groups: culture_ID, 15
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 3.595 0.234 16.432 15.4
## day -0.136 0.011 21.354 -12.4
## patch_typeSmall connected to large 0.352 0.405 16.432 0.9
## day:patch_typeSmall connected to large 0.009 0.019 21.354 0.5
## Pr(>|t|)
## (Intercept) 4e-11 ***
## day 3e-11 ***
## patch_typeSmall connected to large 0.4
## day:patch_typeSmall connected to large 0.7
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) day p_Sctl
## day -0.952
## ptch_tySctl -0.577 0.550
## dy:ptc_Sctl 0.550 -0.577 -0.952
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')Click for model ANOVA
anova(full_model)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## day 43.056 43.056 1 21.354 191.7506 3.851e-12 ***
## patch_type 0.170 0.170 1 16.432 0.7565 0.3970
## day:patch_type 0.046 0.046 1 21.354 0.2058 0.6546
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#Create resid vs fitted (if your number of fitted values is lower than the number of rows in the filtered dataset, it's probably because you have NAs)
res_vs_fit = filtered_data %>%
mutate(predicted = fitted(full_model),
residuals = resid(full_model)) %>%
plot_ly(x = ~predicted,
y = ~residuals,
type = "scatter",
mode = "markers",
marker = list(size = 5, color = "#4C78A8"),
text = paste(" System nr: ", filtered_data$culture_ID, "<br>",
"Day: ", filtered_data$day, "<br>",
"Meta-ecosystem: ", filtered_data$patch_type, "<br>",
"Tot Biomass: ", round(filtered_data$bioarea_mm2_per_ml, digits = 2), "<br>"),
hoverinfo = "text") %>%
plotly::layout(
xaxis = list(title = "Fitted Values"),
yaxis = list(title = "Residuals")
)Click for residual plots
res_vs_fitqqnorm(resid(full_model))
model_stats_full = compute.model.stats(full_model,
null_model,
"mixed_model")
model_stats_full %>%
mutate(deltaAIC = round(deltaAIC, digits = 1),
p_value = round(p_value, digits = 3),
R2 = NULL,
evidence = "",
evidence = ifelse(p_value > 0.1,
"none",
evidence),
evidence = ifelse(p_value < 0.1,
"* weak",
evidence),
evidence = ifelse(p_value < 0.05,
"** moderate",
evidence),
evidence = ifelse(p_value < 0.01,
"*** strong",
evidence),
evidence = ifelse(p_value < 0.001,
"**** very strong",
evidence),
p_value = ifelse(p_value < 0.001,
"< 0.001",
p_value)) %>%
print()## deltaAIC p_value evidence
## 1 -10.4 0.001 *** strong
patch_type)
reduced_model = lmer(
get(response_variable) ~
day +
patch_type +
(day | culture_ID),
data = filtered_data,
REML = FALSE,
control = lmerControl(optimizer = "Nelder_Mead")
)Click for model output
print(summary(reduced_model), digits = 1)## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
## method [lmerModLmerTest]
## Formula: get(response_variable) ~ day + patch_type + (day | culture_ID)
## Data: filtered_data
## Control: lmerControl(optimizer = "Nelder_Mead")
##
## AIC BIC logLik deviance df.resid
## 145.8 163.3 -65.9 131.8 83
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.98 -0.74 0.04 0.72 2.13
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## culture_ID (Intercept) 3e-01 0.51
## day 4e-04 0.02 -1.00
## Residual 2e-01 0.47
## Number of obs: 90, groups: culture_ID, 15
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.537 0.196 17.527 18 9e-13
## day -0.133 0.009 21.095 -15 1e-12
## patch_typeSmall connected to large 0.528 0.123 32.918 4 2e-04
##
## (Intercept) ***
## day ***
## patch_typeSmall connected to large ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) day
## day -0.932
## ptch_tySctl -0.210 0.000
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')#Create resid vs fitted
res_vs_fit = filtered_data %>%
mutate(predicted = fitted(reduced_model),
residuals = resid(reduced_model)) %>%
plot_ly(x = ~predicted,
y = ~residuals,
type = "scatter",
mode = "markers",
marker = list(size = 5, color = "#4C78A8"),
text = paste(" System nr: ", filtered_data$culture_ID, "<br>",
"Day: ", filtered_data$day, "<br>",
"Meta-ecosystem: ", filtered_data$patch_type, "<br>",
"Tot Biomass: ", round(filtered_data$bioarea_mm2_per_ml, digits = 2), "<br>"),
hoverinfo = "text") %>%
plotly::layout(
xaxis = list(title = "Fitted Values"),
yaxis = list(title = "Residuals")
)Click for residual plots
res_vs_fitqqnorm(resid(reduced_model))
model_stats_fixed = compute.model.stats(reduced_model,
null_model,
"mixed_model")
model_stats_fixed %>%
mutate(deltaAIC = round(deltaAIC, digits = 1),
p_value = round(p_value, digits = 3),
R2 = NULL,
evidence = "",
evidence = ifelse(p_value > 0.1,
"none",
evidence),
evidence = ifelse(p_value < 0.1,
"* weak",
evidence),
evidence = ifelse(p_value < 0.05,
"** moderate",
evidence),
evidence = ifelse(p_value < 0.01,
"*** strong",
evidence),
evidence = ifelse(p_value < 0.001,
"**** very strong",
evidence),
p_value = ifelse(p_value < 0.001,
"< 0.001",
p_value)) %>%
print()## deltaAIC p_value evidence
## 1 -12.2 < 0.001 **** very strongresults_table = fill.results.table(
results_table,
response_variable,
patch_type_i,
model_stats_full,
model_stats_fixed
)filtered_data = ds_patches %>%
filter(time_point >= first_time_point_model,
time_point <= last_time_point_model,
patch_type %in% patch_type_i,
!is.na(!!sym(response_variable)))plot.patches.points(filtered_data,
patch_type_i,
response_variable)
null_model = lmer(
get(response_variable) ~
day +
(day | culture_ID),
data = filtered_data,
REML = FALSE,
control = lmerControl(optimizer = "Nelder_Mead")
)patch_type and
patch_type : day)
full_model = lmer(
get(response_variable) ~
day +
patch_type +
patch_type : day +
(day | culture_ID),
data = filtered_data,
REML = FALSE,
control = lmerControl(optimizer = "Nelder_Mead")
)Click for model output
print(summary(full_model), digits = 1)## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
## method [lmerModLmerTest]
## Formula: get(response_variable) ~ day + patch_type + patch_type:day +
## (day | culture_ID)
## Data: filtered_data
## Control: lmerControl(optimizer = "Nelder_Mead")
##
## AIC BIC logLik deviance df.resid
## 147.6 167.6 -65.8 131.6 82
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.99 -0.75 0.07 0.67 2.18
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## culture_ID (Intercept) 2e-01 0.50
## day 4e-04 0.02 -1.00
## Residual 2e-01 0.47
## Number of obs: 90, groups: culture_ID, 15
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 3.595 0.234 16.432 15.4
## day -0.136 0.011 21.354 -12.4
## patch_typeSmall connected to large 0.352 0.405 16.432 0.9
## day:patch_typeSmall connected to large 0.009 0.019 21.354 0.5
## Pr(>|t|)
## (Intercept) 4e-11 ***
## day 3e-11 ***
## patch_typeSmall connected to large 0.4
## day:patch_typeSmall connected to large 0.7
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) day p_Sctl
## day -0.952
## ptch_tySctl -0.577 0.550
## dy:ptc_Sctl 0.550 -0.577 -0.952
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')Click for model ANOVA
anova(full_model)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## day 43.056 43.056 1 21.354 191.7506 3.851e-12 ***
## patch_type 0.170 0.170 1 16.432 0.7565 0.3970
## day:patch_type 0.046 0.046 1 21.354 0.2058 0.6546
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#Create resid vs fitted (if your number of fitted values is lower than the number of rows in the filtered dataset, it's probably because you have NAs)
res_vs_fit = filtered_data %>%
mutate(predicted = fitted(full_model),
residuals = resid(full_model)) %>%
plot_ly(x = ~predicted,
y = ~residuals,
type = "scatter",
mode = "markers",
marker = list(size = 5, color = "#4C78A8"),
text = paste(" System nr: ", filtered_data$culture_ID, "<br>",
"Day: ", filtered_data$day, "<br>",
"Meta-ecosystem: ", filtered_data$patch_type, "<br>",
"Tot Biomass: ", round(filtered_data$bioarea_mm2_per_ml, digits = 2), "<br>"),
hoverinfo = "text") %>%
plotly::layout(
xaxis = list(title = "Fitted Values"),
yaxis = list(title = "Residuals")
)Click for residual plots
res_vs_fitqqnorm(resid(full_model))model_stats_full = compute.model.stats(full_model,
null_model,
"mixed_model")
model_stats_full %>%
mutate(deltaAIC = round(deltaAIC, digits = 1),
p_value = round(p_value, digits = 3),
R2 = NULL,
evidence = "",
evidence = ifelse(p_value > 0.1,
"none",
evidence),
evidence = ifelse(p_value < 0.1,
"* weak",
evidence),
evidence = ifelse(p_value < 0.05,
"** moderate",
evidence),
evidence = ifelse(p_value < 0.01,
"*** strong",
evidence),
evidence = ifelse(p_value < 0.001,
"**** very strong",
evidence),
p_value = ifelse(p_value < 0.001,
"< 0.001",
p_value)) %>%
print()## deltaAIC p_value evidence
## 1 -10.4 0.001 *** strong
patch_type)
reduced_model = lmer(
get(response_variable) ~
day +
patch_type +
(day | culture_ID),
data = filtered_data,
REML = FALSE,
control = lmerControl(optimizer = "Nelder_Mead")
)Click for model output
print(summary(reduced_model), digits = 1)## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
## method [lmerModLmerTest]
## Formula: get(response_variable) ~ day + patch_type + (day | culture_ID)
## Data: filtered_data
## Control: lmerControl(optimizer = "Nelder_Mead")
##
## AIC BIC logLik deviance df.resid
## 145.8 163.3 -65.9 131.8 83
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.98 -0.74 0.04 0.72 2.13
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## culture_ID (Intercept) 3e-01 0.51
## day 4e-04 0.02 -1.00
## Residual 2e-01 0.47
## Number of obs: 90, groups: culture_ID, 15
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.537 0.196 17.527 18 9e-13
## day -0.133 0.009 21.095 -15 1e-12
## patch_typeSmall connected to large 0.528 0.123 32.918 4 2e-04
##
## (Intercept) ***
## day ***
## patch_typeSmall connected to large ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) day
## day -0.932
## ptch_tySctl -0.210 0.000
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')#Create resid vs fitted
res_vs_fit = filtered_data %>%
mutate(predicted = fitted(reduced_model),
residuals = resid(reduced_model)) %>%
plot_ly(x = ~predicted,
y = ~residuals,
type = "scatter",
mode = "markers",
marker = list(size = 5, color = "#4C78A8"),
text = paste(" System nr: ", filtered_data$culture_ID, "<br>",
"Day: ", filtered_data$day, "<br>",
"Meta-ecosystem: ", filtered_data$patch_type, "<br>",
"Tot Biomass: ", round(filtered_data$bioarea_mm2_per_ml, digits = 2), "<br>"),
hoverinfo = "text") %>%
plotly::layout(
xaxis = list(title = "Fitted Values"),
yaxis = list(title = "Residuals")
)Click for residual plots
res_vs_fitqqnorm(resid(reduced_model))model_stats_fixed = compute.model.stats(reduced_model,
null_model,
"mixed_model")
model_stats_fixed %>%
mutate(deltaAIC = round(deltaAIC, digits = 1),
p_value = round(p_value, digits = 3),
R2 = NULL,
evidence = "",
evidence = ifelse(p_value > 0.1,
"none",
evidence),
evidence = ifelse(p_value < 0.1,
"* weak",
evidence),
evidence = ifelse(p_value < 0.05,
"** moderate",
evidence),
evidence = ifelse(p_value < 0.01,
"*** strong",
evidence),
evidence = ifelse(p_value < 0.001,
"**** very strong",
evidence),
p_value = ifelse(p_value < 0.001,
"< 0.001",
p_value)) %>%
print()## deltaAIC p_value evidence
## 1 -12.2 < 0.001 **** very strongresults_table = fill.results.table(
results_table,
response_variable,
patch_type_i,
model_stats_full,
model_stats_fixed
)Shannon
response_variable = "shannon"filtered_data = ds_patches %>%
filter(time_point >= first_time_point_model,
time_point <= last_time_point_model,
patch_type %in% patch_type_i,
!is.na(!!sym(response_variable)))plot.patches.points(filtered_data,
patch_type_i,
response_variable) 
null_model = lmer(
get(response_variable) ~
day +
(day | culture_ID),
data = filtered_data,
REML = FALSE,
control = lmerControl(optimizer = "Nelder_Mead")
)patch_type and
patch_type : day)
full_model = lmer(
get(response_variable) ~
day +
patch_type +
patch_type : day +
(day | culture_ID),
data = filtered_data,
REML = FALSE,
control = lmerControl(optimizer = "Nelder_Mead")
)Click for model output
print(summary(full_model), digits = 1)## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
## method [lmerModLmerTest]
## Formula: get(response_variable) ~ day + patch_type + patch_type:day +
## (day | culture_ID)
## Data: filtered_data
## Control: lmerControl(optimizer = "Nelder_Mead")
##
## AIC BIC logLik deviance df.resid
## 61.6 81.6 -22.8 45.6 82
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.3 -0.5 0.1 0.5 2.2
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## culture_ID (Intercept) 1e-03 0.032
## day 1e-05 0.004 -1.00
## Residual 1e-01 0.309
## Number of obs: 90, groups: culture_ID, 15
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 1.742 0.113 76.059 15
## day -0.043 0.006 53.128 -7
## patch_typeSmall connected to large -0.355 0.195 76.059 -2
## day:patch_typeSmall connected to large 0.043 0.010 53.128 4
## Pr(>|t|)
## (Intercept) <2e-16 ***
## day 1e-09 ***
## patch_typeSmall connected to large 0.07 .
## day:patch_typeSmall connected to large 1e-04 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) day p_Sctl
## day -0.930
## ptch_tySctl -0.577 0.537
## dy:ptc_Sctl 0.537 -0.577 -0.930
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')Click for model ANOVA
anova(full_model)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## day 1.75046 1.75046 1 53.128 18.3720 7.684e-05 ***
## patch_type 0.31502 0.31502 1 76.059 3.3063 0.0729497 .
## day:patch_type 1.63469 1.63469 1 53.128 17.1569 0.0001239 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#Create resid vs fitted (if your number of fitted values is lower than the number of rows in the filtered dataset, it's probably because you have NAs)
res_vs_fit = filtered_data %>%
mutate(predicted = fitted(full_model),
residuals = resid(full_model)) %>%
plot_ly(x = ~predicted,
y = ~residuals,
type = "scatter",
mode = "markers",
marker = list(size = 5, color = "#4C78A8"),
text = paste(" System nr: ", filtered_data$culture_ID, "<br>",
"Day: ", filtered_data$day, "<br>",
"Meta-ecosystem: ", filtered_data$patch_type, "<br>",
"Tot Biomass: ", round(filtered_data$bioarea_mm2_per_ml, digits = 2), "<br>"),
hoverinfo = "text") %>%
plotly::layout(
xaxis = list(title = "Fitted Values"),
yaxis = list(title = "Residuals")
)Click for residual plots
res_vs_fitqqnorm(resid(full_model))
model_stats_full = compute.model.stats(full_model,
null_model,
"mixed_model")
model_stats_full %>%
mutate(deltaAIC = round(deltaAIC, digits = 1),
p_value = round(p_value, digits = 3),
R2 = NULL,
evidence = "",
evidence = ifelse(p_value > 0.1,
"none",
evidence),
evidence = ifelse(p_value < 0.1,
"* weak",
evidence),
evidence = ifelse(p_value < 0.05,
"** moderate",
evidence),
evidence = ifelse(p_value < 0.01,
"*** strong",
evidence),
evidence = ifelse(p_value < 0.001,
"**** very strong",
evidence),
p_value = ifelse(p_value < 0.001,
"< 0.001",
p_value)) %>%
print()## deltaAIC p_value evidence
## 1 -17.5 < 0.001 **** very strong
patch_type)
reduced_model = lmer(
get(response_variable) ~
day +
patch_type +
(day | culture_ID),
data = filtered_data,
REML = FALSE,
control = lmerControl(optimizer = "Nelder_Mead")
)Click for model output
print(summary(reduced_model), digits = 1)## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
## method [lmerModLmerTest]
## Formula: get(response_variable) ~ day + patch_type + (day | culture_ID)
## Data: filtered_data
## Control: lmerControl(optimizer = "Nelder_Mead")
##
## AIC BIC logLik deviance df.resid
## 72.8 90.3 -29.4 58.8 83
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2 -0.5 0.2 0.6 2.1
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## culture_ID (Intercept) 6e-02 0.24
## day 3e-04 0.02 -1.00
## Residual 1e-01 0.32
## Number of obs: 90, groups: culture_ID, 15
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.510 0.115 23.192 13 3e-12
## day -0.029 0.007 15.944 -4 4e-04
## patch_typeSmall connected to large 0.341 0.075 39.923 4 5e-05
##
## (Intercept) ***
## day ***
## patch_typeSmall connected to large ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) day
## day -0.926
## ptch_tySctl -0.218 0.000
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')#Create resid vs fitted
res_vs_fit = filtered_data %>%
mutate(predicted = fitted(reduced_model),
residuals = resid(reduced_model)) %>%
plot_ly(x = ~predicted,
y = ~residuals,
type = "scatter",
mode = "markers",
marker = list(size = 5, color = "#4C78A8"),
text = paste(" System nr: ", filtered_data$culture_ID, "<br>",
"Day: ", filtered_data$day, "<br>",
"Meta-ecosystem: ", filtered_data$patch_type, "<br>",
"Tot Biomass: ", round(filtered_data$bioarea_mm2_per_ml, digits = 2), "<br>"),
hoverinfo = "text") %>%
plotly::layout(
xaxis = list(title = "Fitted Values"),
yaxis = list(title = "Residuals")
)Click for residual plots
res_vs_fitqqnorm(resid(reduced_model))
model_stats_fixed = compute.model.stats(reduced_model,
null_model,
"mixed_model")
model_stats_fixed %>%
mutate(deltaAIC = round(deltaAIC, digits = 1),
p_value = round(p_value, digits = 3),
R2 = NULL,
evidence = "",
evidence = ifelse(p_value > 0.1,
"none",
evidence),
evidence = ifelse(p_value < 0.1,
"* weak",
evidence),
evidence = ifelse(p_value < 0.05,
"** moderate",
evidence),
evidence = ifelse(p_value < 0.01,
"*** strong",
evidence),
evidence = ifelse(p_value < 0.001,
"**** very strong",
evidence),
p_value = ifelse(p_value < 0.001,
"< 0.001",
p_value)) %>%
print()## deltaAIC p_value evidence
## 1 -6.3 0.004 *** strongresults_table = fill.results.table(
results_table,
response_variable,
patch_type_i,
model_stats_full,
model_stats_fixed
)Richness
response_variable = "species_richness"filtered_data = ds_patches %>%
filter(time_point >= first_time_point_model,
time_point <= last_time_point_model,
patch_type %in% patch_type_i,
!is.na(!!sym(response_variable)))plot.patches.points(filtered_data,
patch_type_i,
response_variable) 
null_model = lmer(
get(response_variable) ~
day +
(day | culture_ID),
data = filtered_data,
REML = FALSE,
control = lmerControl(optimizer = "Nelder_Mead")
)patch_type and
patch_type : day)
full_model = lmer(
get(response_variable) ~
day +
patch_type +
patch_type : day +
(day | culture_ID),
data = filtered_data,
REML = FALSE,
control = lmerControl(optimizer = "Nelder_Mead")
)Click for model output
print(summary(full_model), digits = 1)## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
## method [lmerModLmerTest]
## Formula: get(response_variable) ~ day + patch_type + patch_type:day +
## (day | culture_ID)
## Data: filtered_data
## Control: lmerControl(optimizer = "Nelder_Mead")
##
## AIC BIC logLik deviance df.resid
## 292.1 312.1 -138.1 276.1 82
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.9 -0.6 -0.1 0.5 2.7
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## culture_ID (Intercept) 0e+00 0e+00
## day 2e-16 1e-08 NaN
## Residual 1e+00 1e+00
## Number of obs: 90, groups: culture_ID, 15
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 7.69 0.41 90.00 18.8
## day -0.22 0.02 90.00 -10.1
## patch_typeSmall connected to large -0.48 0.71 90.00 -0.7
## day:patch_typeSmall connected to large 0.12 0.04 90.00 3.2
## Pr(>|t|)
## (Intercept) <2e-16 ***
## day <2e-16 ***
## patch_typeSmall connected to large 0.500
## day:patch_typeSmall connected to large 0.002 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) day p_Sctl
## day -0.935
## ptch_tySctl -0.577 0.540
## dy:ptc_Sctl 0.540 -0.577 -0.935
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')Click for model ANOVA
anova(full_model)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## day 91.772 91.772 1 90 72.8881 3.151e-13 ***
## patch_type 0.578 0.578 1 90 0.4589 0.499857
## day:patch_type 12.652 12.652 1 90 10.0485 0.002084 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#Create resid vs fitted (if your number of fitted values is lower than the number of rows in the filtered dataset, it's probably because you have NAs)
res_vs_fit = filtered_data %>%
mutate(predicted = fitted(full_model),
residuals = resid(full_model)) %>%
plot_ly(x = ~predicted,
y = ~residuals,
type = "scatter",
mode = "markers",
marker = list(size = 5, color = "#4C78A8"),
text = paste(" System nr: ", filtered_data$culture_ID, "<br>",
"Day: ", filtered_data$day, "<br>",
"Meta-ecosystem: ", filtered_data$patch_type, "<br>",
"Tot Biomass: ", round(filtered_data$bioarea_mm2_per_ml, digits = 2), "<br>"),
hoverinfo = "text") %>%
plotly::layout(
xaxis = list(title = "Fitted Values"),
yaxis = list(title = "Residuals")
)Click for residual plots
res_vs_fitqqnorm(resid(full_model))
model_stats_full = compute.model.stats(full_model,
null_model,
"mixed_model")
model_stats_full %>%
mutate(deltaAIC = round(deltaAIC, digits = 1),
p_value = round(p_value, digits = 3),
R2 = NULL,
evidence = "",
evidence = ifelse(p_value > 0.1,
"none",
evidence),
evidence = ifelse(p_value < 0.1,
"* weak",
evidence),
evidence = ifelse(p_value < 0.05,
"** moderate",
evidence),
evidence = ifelse(p_value < 0.01,
"*** strong",
evidence),
evidence = ifelse(p_value < 0.001,
"**** very strong",
evidence),
p_value = ifelse(p_value < 0.001,
"< 0.001",
p_value)) %>%
print()## deltaAIC p_value evidence
## 1 -20.5 < 0.001 **** very strong
patch_type)
reduced_model = lmer(
get(response_variable) ~
day +
patch_type +
(day | culture_ID),
data = filtered_data,
REML = FALSE,
control = lmerControl(optimizer = "Nelder_Mead")
)Click for model output
print(summary(reduced_model), digits = 1)## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
## method [lmerModLmerTest]
## Formula: get(response_variable) ~ day + patch_type + (day | culture_ID)
## Data: filtered_data
## Control: lmerControl(optimizer = "Nelder_Mead")
##
## AIC BIC logLik deviance df.resid
## 299.1 316.6 -142.6 285.1 83
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.83 -0.68 -0.06 0.50 2.54
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## culture_ID (Intercept) 0.619 0.79
## day 0.001 0.04 -1.00
## Residual 1.323 1.15
## Number of obs: 90, groups: culture_ID, 15
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 6.97 0.41 14.53 17 5e-11
## day -0.18 0.02 15.66 -9 2e-07
## patch_typeSmall connected to large 1.68 0.26 49.18 6 5e-08
##
## (Intercept) ***
## day ***
## patch_typeSmall connected to large ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) day
## day -0.929
## ptch_tySctl -0.214 0.000
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')#Create resid vs fitted
res_vs_fit = filtered_data %>%
mutate(predicted = fitted(reduced_model),
residuals = resid(reduced_model)) %>%
plot_ly(x = ~predicted,
y = ~residuals,
type = "scatter",
mode = "markers",
marker = list(size = 5, color = "#4C78A8"),
text = paste(" System nr: ", filtered_data$culture_ID, "<br>",
"Day: ", filtered_data$day, "<br>",
"Meta-ecosystem: ", filtered_data$patch_type, "<br>",
"Tot Biomass: ", round(filtered_data$bioarea_mm2_per_ml, digits = 2), "<br>"),
hoverinfo = "text") %>%
plotly::layout(
xaxis = list(title = "Fitted Values"),
yaxis = list(title = "Residuals")
)Click for residual plots
res_vs_fitqqnorm(resid(reduced_model))
model_stats_fixed = compute.model.stats(reduced_model,
null_model,
"mixed_model")
model_stats_fixed %>%
mutate(deltaAIC = round(deltaAIC, digits = 1),
p_value = round(p_value, digits = 3),
R2 = NULL,
evidence = "",
evidence = ifelse(p_value > 0.1,
"none",
evidence),
evidence = ifelse(p_value < 0.1,
"* weak",
evidence),
evidence = ifelse(p_value < 0.05,
"** moderate",
evidence),
evidence = ifelse(p_value < 0.01,
"*** strong",
evidence),
evidence = ifelse(p_value < 0.001,
"**** very strong",
evidence),
p_value = ifelse(p_value < 0.001,
"< 0.001",
p_value)) %>%
print()## deltaAIC p_value evidence
## 1 -13.5 < 0.001 **** very strongresults_table = fill.results.table(
results_table,
response_variable,
patch_type_i,
model_stats_full,
model_stats_fixed
)Evenness
response_variable = "evenness_pielou"filtered_data = ds_patches %>%
filter(time_point >= first_time_point_model,
time_point <= last_time_point_model,
patch_type %in% patch_type_i,
!is.na(!!sym(response_variable)))plot.patches.points(filtered_data,
patch_type_i,
response_variable) 
null_model = lmer(
get(response_variable) ~
day +
(day | culture_ID),
data = filtered_data,
REML = FALSE,
control = lmerControl(optimizer = "Nelder_Mead")
)patch_type and
patch_type : day)
full_model = lmer(
get(response_variable) ~
day +
patch_type +
patch_type : day +
(day | culture_ID),
data = filtered_data,
REML = FALSE,
control = lmerControl(optimizer = "Nelder_Mead")
)Click for model output
print(summary(full_model), digits = 1)## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
## method [lmerModLmerTest]
## Formula: get(response_variable) ~ day + patch_type + patch_type:day +
## (day | culture_ID)
## Data: filtered_data
## Control: lmerControl(optimizer = "Nelder_Mead")
##
## AIC BIC logLik deviance df.resid
## -115.6 -96.2 65.8 -131.6 76
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.1 -0.4 0.1 0.7 1.7
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## culture_ID (Intercept) 0e+00 0e+00
## day 7e-18 3e-09 NaN
## Residual 1e-02 1e-01
## Number of obs: 84, groups: culture_ID, 15
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 0.743 0.042 84.000 18
## day 0.004 0.002 84.000 2
## patch_typeSmall connected to large -0.071 0.070 84.000 -1
## day:patch_typeSmall connected to large 0.005 0.004 84.000 1
## Pr(>|t|)
## (Intercept) <2e-16 ***
## day 0.07 .
## patch_typeSmall connected to large 0.31
## day:patch_typeSmall connected to large 0.22
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) day p_Sctl
## day -0.932
## ptch_tySctl -0.590 0.550
## dy:ptc_Sctl 0.561 -0.602 -0.934
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')Click for model ANOVA
anova(full_model)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## day 0.142288 0.142288 1 84 11.6451 0.0009925 ***
## patch_type 0.012547 0.012547 1 84 1.0269 0.3138074
## day:patch_type 0.018337 0.018337 1 84 1.5007 0.2239857
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#Create resid vs fitted (if your number of fitted values is lower than the number of rows in the filtered dataset, it's probably because you have NAs)
res_vs_fit = filtered_data %>%
mutate(predicted = fitted(full_model),
residuals = resid(full_model)) %>%
plot_ly(x = ~predicted,
y = ~residuals,
type = "scatter",
mode = "markers",
marker = list(size = 5, color = "#4C78A8"),
text = paste(" System nr: ", filtered_data$culture_ID, "<br>",
"Day: ", filtered_data$day, "<br>",
"Meta-ecosystem: ", filtered_data$patch_type, "<br>",
"Tot Biomass: ", round(filtered_data$bioarea_mm2_per_ml, digits = 2), "<br>"),
hoverinfo = "text") %>%
plotly::layout(
xaxis = list(title = "Fitted Values"),
yaxis = list(title = "Residuals")
)Click for residual plots
res_vs_fitqqnorm(resid(full_model))
model_stats_full = compute.model.stats(full_model,
null_model,
"mixed_model")
model_stats_full %>%
mutate(deltaAIC = round(deltaAIC, digits = 1),
p_value = round(p_value, digits = 3),
R2 = NULL,
evidence = "",
evidence = ifelse(p_value > 0.1,
"none",
evidence),
evidence = ifelse(p_value < 0.1,
"* weak",
evidence),
evidence = ifelse(p_value < 0.05,
"** moderate",
evidence),
evidence = ifelse(p_value < 0.01,
"*** strong",
evidence),
evidence = ifelse(p_value < 0.001,
"**** very strong",
evidence),
p_value = ifelse(p_value < 0.001,
"< 0.001",
p_value)) %>%
print()## deltaAIC p_value evidence
## 1 2.4 0.445 none
patch_type)
reduced_model = lmer(
get(response_variable) ~
day +
patch_type +
(day | culture_ID),
data = filtered_data,
REML = FALSE,
control = lmerControl(optimizer = "Nelder_Mead")
)Click for model output
print(summary(reduced_model), digits = 1)## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
## method [lmerModLmerTest]
## Formula: get(response_variable) ~ day + patch_type + (day | culture_ID)
## Data: filtered_data
## Control: lmerControl(optimizer = "Nelder_Mead")
##
## AIC BIC logLik deviance df.resid
## -116.1 -99.1 65.1 -130.1 77
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.1 -0.5 0.1 0.7 1.8
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## culture_ID (Intercept) 0e+00 0e+00
## day 1e-15 3e-08 NaN
## Residual 1e-02 1e-01
## Number of obs: 84, groups: culture_ID, 15
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 7e-01 4e-02 8e+01 20.6 <2e-16
## day 6e-03 2e-03 8e+01 3.2 0.002
## patch_typeSmall connected to large 9e-03 3e-02 8e+01 0.4 0.718
##
## (Intercept) ***
## day **
## patch_typeSmall connected to large
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) day
## day -0.899
## ptch_tySctl -0.224 -0.042
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')#Create resid vs fitted
res_vs_fit = filtered_data %>%
mutate(predicted = fitted(reduced_model),
residuals = resid(reduced_model)) %>%
plot_ly(x = ~predicted,
y = ~residuals,
type = "scatter",
mode = "markers",
marker = list(size = 5, color = "#4C78A8"),
text = paste(" System nr: ", filtered_data$culture_ID, "<br>",
"Day: ", filtered_data$day, "<br>",
"Meta-ecosystem: ", filtered_data$patch_type, "<br>",
"Tot Biomass: ", round(filtered_data$bioarea_mm2_per_ml, digits = 2), "<br>"),
hoverinfo = "text") %>%
plotly::layout(
xaxis = list(title = "Fitted Values"),
yaxis = list(title = "Residuals")
)Click for residual plots
res_vs_fitqqnorm(resid(reduced_model))
model_stats_fixed = compute.model.stats(reduced_model,
null_model,
"mixed_model")
model_stats_fixed %>%
mutate(deltaAIC = round(deltaAIC, digits = 1),
p_value = round(p_value, digits = 3),
R2 = NULL,
evidence = "",
evidence = ifelse(p_value > 0.1,
"none",
evidence),
evidence = ifelse(p_value < 0.1,
"* weak",
evidence),
evidence = ifelse(p_value < 0.05,
"** moderate",
evidence),
evidence = ifelse(p_value < 0.01,
"*** strong",
evidence),
evidence = ifelse(p_value < 0.001,
"**** very strong",
evidence),
p_value = ifelse(p_value < 0.001,
"< 0.001",
p_value)) %>%
print()## deltaAIC p_value evidence
## 1 1.9 0.717 noneresults_table = fill.results.table(
results_table,
response_variable,
patch_type_i,
model_stats_full,
model_stats_fixed
)Median Size
response_variable = "median_body_area_µm2"filtered_data = ds_patches %>%
filter(time_point >= first_time_point_model,
time_point <= last_time_point_model,
patch_type %in% patch_type_i,
!is.na(!!sym(response_variable)))plot.patches.points(filtered_data,
patch_type_i,
response_variable) 
null_model = lmer(
get(response_variable) ~
day +
(day | culture_ID),
data = filtered_data,
REML = FALSE,
control = lmerControl(optimizer = "Nelder_Mead")
)patch_type and
patch_type : day)
full_model = lmer(
get(response_variable) ~
day +
patch_type +
patch_type : day +
(day | culture_ID),
data = filtered_data,
REML = FALSE,
control = lmerControl(optimizer = "Nelder_Mead")
)Click for model output
print(summary(full_model), digits = 1)## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
## method [lmerModLmerTest]
## Formula: get(response_variable) ~ day + patch_type + patch_type:day +
## (day | culture_ID)
## Data: filtered_data
## Control: lmerControl(optimizer = "Nelder_Mead")
##
## AIC BIC logLik deviance df.resid
## 1420.1 1440.1 -702.1 1404.1 82
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.90 -0.66 0.02 0.73 1.90
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## culture_ID (Intercept) 9e+04 307
## day 3e+02 19 -1.00
## Residual 3e+05 578
## Number of obs: 90, groups: culture_ID, 15
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 3e+03 2e+02 2e+01 13.2
## day 4e+01 1e+01 2e+01 2.9
## patch_typeSmall connected to large 1e+02 4e+02 2e+01 0.3
## day:patch_typeSmall connected to large 5e-01 2e+01 2e+01 0.0
## Pr(>|t|)
## (Intercept) 1e-11 ***
## day 0.01 **
## patch_typeSmall connected to large 0.75
## day:patch_typeSmall connected to large 0.98
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) day p_Sctl
## day -0.946
## ptch_tySctl -0.577 0.546
## dy:ptc_Sctl 0.546 -0.577 -0.946
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')Click for model ANOVA
anova(full_model)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## day 3897822 3897822 1 15.70 11.6655 0.003625 **
## patch_type 34717 34717 1 20.88 0.1039 0.750402
## day:patch_type 216 216 1 15.70 0.0006 0.980048
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#Create resid vs fitted (if your number of fitted values is lower than the number of rows in the filtered dataset, it's probably because you have NAs)
res_vs_fit = filtered_data %>%
mutate(predicted = fitted(full_model),
residuals = resid(full_model)) %>%
plot_ly(x = ~predicted,
y = ~residuals,
type = "scatter",
mode = "markers",
marker = list(size = 5, color = "#4C78A8"),
text = paste(" System nr: ", filtered_data$culture_ID, "<br>",
"Day: ", filtered_data$day, "<br>",
"Meta-ecosystem: ", filtered_data$patch_type, "<br>",
"Tot Biomass: ", round(filtered_data$bioarea_mm2_per_ml, digits = 2), "<br>"),
hoverinfo = "text") %>%
plotly::layout(
xaxis = list(title = "Fitted Values"),
yaxis = list(title = "Residuals")
)Click for residual plots
res_vs_fitqqnorm(resid(full_model))
model_stats_full = compute.model.stats(full_model,
null_model,
"mixed_model")
model_stats_full %>%
mutate(deltaAIC = round(deltaAIC, digits = 1),
p_value = round(p_value, digits = 3),
R2 = NULL,
evidence = "",
evidence = ifelse(p_value > 0.1,
"none",
evidence),
evidence = ifelse(p_value < 0.1,
"* weak",
evidence),
evidence = ifelse(p_value < 0.05,
"** moderate",
evidence),
evidence = ifelse(p_value < 0.01,
"*** strong",
evidence),
evidence = ifelse(p_value < 0.001,
"**** very strong",
evidence),
p_value = ifelse(p_value < 0.001,
"< 0.001",
p_value)) %>%
print()## deltaAIC p_value evidence
## 1 2.9 0.567 none
patch_type)
reduced_model = lmer(
get(response_variable) ~
day +
patch_type +
(day | culture_ID),
data = filtered_data,
REML = FALSE,
control = lmerControl(optimizer = "Nelder_Mead")
)Click for model output
print(summary(reduced_model), digits = 1)## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
## method [lmerModLmerTest]
## Formula: get(response_variable) ~ day + patch_type + (day | culture_ID)
## Data: filtered_data
## Control: lmerControl(optimizer = "Nelder_Mead")
##
## AIC BIC logLik deviance df.resid
## 1418.1 1435.6 -702.1 1404.1 83
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.90 -0.66 0.02 0.73 1.89
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## culture_ID (Intercept) 9e+04 307
## day 3e+02 19 -1.00
## Residual 3e+05 578
## Number of obs: 90, groups: culture_ID, 15
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3056 194 23 16 8e-14
## day 37 10 16 4 0.002
## patch_typeSmall connected to large 139 130 70 1 0.288
##
## (Intercept) ***
## day **
## patch_typeSmall connected to large
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) day
## day -0.922
## ptch_tySctl -0.223 0.000
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')#Create resid vs fitted
res_vs_fit = filtered_data %>%
mutate(predicted = fitted(reduced_model),
residuals = resid(reduced_model)) %>%
plot_ly(x = ~predicted,
y = ~residuals,
type = "scatter",
mode = "markers",
marker = list(size = 5, color = "#4C78A8"),
text = paste(" System nr: ", filtered_data$culture_ID, "<br>",
"Day: ", filtered_data$day, "<br>",
"Meta-ecosystem: ", filtered_data$patch_type, "<br>",
"Tot Biomass: ", round(filtered_data$bioarea_mm2_per_ml, digits = 2), "<br>"),
hoverinfo = "text") %>%
plotly::layout(
xaxis = list(title = "Fitted Values"),
yaxis = list(title = "Residuals")
)Click for residual plots
res_vs_fitqqnorm(resid(reduced_model))
model_stats_fixed = compute.model.stats(reduced_model,
null_model,
"mixed_model")
model_stats_fixed %>%
mutate(deltaAIC = round(deltaAIC, digits = 1),
p_value = round(p_value, digits = 3),
R2 = NULL,
evidence = "",
evidence = ifelse(p_value > 0.1,
"none",
evidence),
evidence = ifelse(p_value < 0.1,
"* weak",
evidence),
evidence = ifelse(p_value < 0.05,
"** moderate",
evidence),
evidence = ifelse(p_value < 0.01,
"*** strong",
evidence),
evidence = ifelse(p_value < 0.001,
"**** very strong",
evidence),
p_value = ifelse(p_value < 0.001,
"< 0.001",
p_value)) %>%
print()## deltaAIC p_value evidence
## 1 0.9 0.287 noneresults_table = fill.results.table(
results_table,
response_variable,
patch_type_i,
model_stats_full,
model_stats_fixed
)Populations
for(patch_type_i in patch_type_i){
print(patch_type_i)
p = ds_patches %>%
filter(patch_type == patch_type_i) %>%
group_by(day) %>%
summarise(
Ble = mean(Ble_indiv_per_ml_dominance, na.rm = TRUE),
# Ble_se = sd(Ble_indiv_per_ml_dominance, na.rm = TRUE) / sqrt(n()),
# Ble_ci = qt(c(0.025, 0.975), df = n() - 1) * Ble_se,
Cep = mean(Cep_indiv_per_ml_dominance, na.rm = TRUE),
Col = mean(Col_indiv_per_ml_dominance, na.rm = TRUE),
Eug = mean(Eug_indiv_per_ml_dominance, na.rm = TRUE),
Eup = mean(Eup_indiv_per_ml_dominance, na.rm = TRUE),
Lox = mean(Lox_indiv_per_ml_dominance, na.rm = TRUE),
Pau = mean(Pau_indiv_per_ml_dominance, na.rm = TRUE),
Pca = mean(Pca_indiv_per_ml_dominance, na.rm = TRUE),
Spi = mean(Spi_indiv_per_ml_dominance, na.rm = TRUE),
Spi_te = mean(Spi_te_indiv_per_ml_dominance, na.rm = TRUE),
Tet = mean(Tet_indiv_per_ml_dominance, na.rm = TRUE)
) %>%
pivot_longer(Ble:Tet, names_to = "species", values_to = "species_indiv_per_ml") %>%
ggplot(aes(
x = day,
y = species_indiv_per_ml,
group = interaction(day, species),
color = species
)) +
geom_point(position = position_dodge(dodging),
size = treatment_points_size) +
geom_line(
aes(group = species),
position = position_dodge(dodging),
linewidth = treatment_lines_linewidth) +
labs(x = axis_names %>%
filter(variable == "day") %>%
pull(axis_name),
y = axis_names %>%
filter(variable == "dominance") %>%
pull(axis_name)) +
coord_cartesian(ylim = c(0, 100))
print(p)
}## [1] "Small connected to large"
## [1] "Small connected to small"
S_S vs S
patch_type_i = c("Small connected to small",
"Small isolated")Biomass
response_variable = "bioarea_mm2_per_ml"filtered_data = ds_patches %>%
filter(time_point >= first_time_point_model,
time_point <= last_time_point_model,
patch_type %in% patch_type_i,
!is.na(!!sym(response_variable)))plot.patches.points(filtered_data,
patch_type_i,
response_variable) 
null_model = lmer(
get(response_variable) ~
day +
(day | culture_ID),
data = filtered_data,
REML = FALSE,
control = lmerControl(optimizer = "Nelder_Mead")
)patch_type and
patch_type : day)
full_model = lmer(
get(response_variable) ~
day +
patch_type +
patch_type : day +
(day | culture_ID),
data = filtered_data,
REML = FALSE,
control = lmerControl(optimizer = "Nelder_Mead")
)Click for model output
print(summary(full_model), digits = 1)## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
## method [lmerModLmerTest]
## Formula: get(response_variable) ~ day + patch_type + patch_type:day +
## (day | culture_ID)
## Data: filtered_data
## Control: lmerControl(optimizer = "Nelder_Mead")
##
## AIC BIC logLik deviance df.resid
## 179.0 199.0 -81.5 163.0 82
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.7 -0.7 0.0 0.6 3.6
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## culture_ID (Intercept) 3e-01 0.51
## day 5e-04 0.02 -1.00
## Residual 3e-01 0.57
## Number of obs: 90, groups: culture_ID, 15
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 3.92 0.37 16.09 10.5
## day -0.15 0.02 21.17 -8.3
## patch_typeSmall connected to small -0.33 0.46 16.09 -0.7
## day:patch_typeSmall connected to small 0.01 0.02 21.17 0.7
## Pr(>|t|)
## (Intercept) 1e-08 ***
## day 4e-08 ***
## patch_typeSmall connected to small 0.5
## day:patch_typeSmall connected to small 0.5
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) day p_Scts
## day -0.952
## ptch_tyScts -0.816 0.777
## dy:ptc_Scts 0.777 -0.816 -0.952
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')Click for model ANOVA
anova(full_model)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## day 54.390 54.390 1 21.167 165.4017 1.797e-11 ***
## patch_type 0.167 0.167 1 16.090 0.5089 0.4859
## day:patch_type 0.147 0.147 1 21.167 0.4457 0.5116
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#Create resid vs fitted (if your number of fitted values is lower than the number of rows in the filtered dataset, it's probably because you have NAs)
res_vs_fit = filtered_data %>%
mutate(predicted = fitted(full_model),
residuals = resid(full_model)) %>%
plot_ly(x = ~predicted,
y = ~residuals,
type = "scatter",
mode = "markers",
marker = list(size = 5, color = "#4C78A8"),
text = paste(" System nr: ", filtered_data$culture_ID, "<br>",
"Day: ", filtered_data$day, "<br>",
"Meta-ecosystem: ", filtered_data$patch_type, "<br>",
"Tot Biomass: ", round(filtered_data$bioarea_mm2_per_ml, digits = 2), "<br>"),
hoverinfo = "text") %>%
plotly::layout(
xaxis = list(title = "Fitted Values"),
yaxis = list(title = "Residuals")
)Click for residual plots
res_vs_fitqqnorm(resid(full_model))
model_stats_full = compute.model.stats(full_model,
null_model,
"mixed_model")
model_stats_full %>%
mutate(deltaAIC = round(deltaAIC, digits = 1),
p_value = round(p_value, digits = 3),
R2 = NULL,
evidence = "",
evidence = ifelse(p_value > 0.1,
"none",
evidence),
evidence = ifelse(p_value < 0.1,
"* weak",
evidence),
evidence = ifelse(p_value < 0.05,
"** moderate",
evidence),
evidence = ifelse(p_value < 0.01,
"*** strong",
evidence),
evidence = ifelse(p_value < 0.001,
"**** very strong",
evidence),
p_value = ifelse(p_value < 0.001,
"< 0.001",
p_value)) %>%
print()## deltaAIC p_value evidence
## 1 3.5 0.778 none
patch_type)
reduced_model = lmer(
get(response_variable) ~
day +
patch_type +
(day | culture_ID),
data = filtered_data,
REML = FALSE,
control = lmerControl(optimizer = "Nelder_Mead")
)Click for model output
print(summary(reduced_model), digits = 1)## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
## method [lmerModLmerTest]
## Formula: get(response_variable) ~ day + patch_type + (day | culture_ID)
## Data: filtered_data
## Control: lmerControl(optimizer = "Nelder_Mead")
##
## AIC BIC logLik deviance df.resid
## 177.5 195.0 -81.7 163.5 83
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.6 -0.7 0.0 0.6 3.7
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## culture_ID (Intercept) 3e-01 0.53
## day 5e-04 0.02 -1.00
## Residual 3e-01 0.57
## Number of obs: 90, groups: culture_ID, 15
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.73 0.24 20.52 15.7 7e-13
## day -0.14 0.01 20.57 -13.3 2e-11
## patch_typeSmall connected to small -0.04 0.14 41.57 -0.2 0.8
##
## (Intercept) ***
## day ***
## patch_typeSmall connected to small
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) day
## day -0.876
## ptch_tyScts -0.393 0.000
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')#Create resid vs fitted
res_vs_fit = filtered_data %>%
mutate(predicted = fitted(reduced_model),
residuals = resid(reduced_model)) %>%
plot_ly(x = ~predicted,
y = ~residuals,
type = "scatter",
mode = "markers",
marker = list(size = 5, color = "#4C78A8"),
text = paste(" System nr: ", filtered_data$culture_ID, "<br>",
"Day: ", filtered_data$day, "<br>",
"Meta-ecosystem: ", filtered_data$patch_type, "<br>",
"Tot Biomass: ", round(filtered_data$bioarea_mm2_per_ml, digits = 2), "<br>"),
hoverinfo = "text") %>%
plotly::layout(
xaxis = list(title = "Fitted Values"),
yaxis = list(title = "Residuals")
)Click for residual plots
res_vs_fitqqnorm(resid(reduced_model))
model_stats_fixed = compute.model.stats(reduced_model,
null_model,
"mixed_model")
model_stats_fixed %>%
mutate(deltaAIC = round(deltaAIC, digits = 1),
p_value = round(p_value, digits = 3),
R2 = NULL,
evidence = "",
evidence = ifelse(p_value > 0.1,
"none",
evidence),
evidence = ifelse(p_value < 0.1,
"* weak",
evidence),
evidence = ifelse(p_value < 0.05,
"** moderate",
evidence),
evidence = ifelse(p_value < 0.01,
"*** strong",
evidence),
evidence = ifelse(p_value < 0.001,
"**** very strong",
evidence),
p_value = ifelse(p_value < 0.001,
"< 0.001",
p_value)) %>%
print()## deltaAIC p_value evidence
## 1 1.9 0.805 noneresults_table = fill.results.table(
results_table,
response_variable,
patch_type_i,
model_stats_full,
model_stats_fixed
)filtered_data = ds_patches %>%
filter(time_point >= first_time_point_model,
time_point <= last_time_point_model,
patch_type %in% patch_type_i,
!is.na(!!sym(response_variable)))plot.patches.points(filtered_data,
patch_type_i,
response_variable)
null_model = lmer(
get(response_variable) ~
day +
(day | culture_ID),
data = filtered_data,
REML = FALSE,
control = lmerControl(optimizer = "Nelder_Mead")
)patch_type and
patch_type : day)
full_model = lmer(
get(response_variable) ~
day +
patch_type +
patch_type : day +
(day | culture_ID),
data = filtered_data,
REML = FALSE,
control = lmerControl(optimizer = "Nelder_Mead")
)Click for model output
print(summary(full_model), digits = 1)## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
## method [lmerModLmerTest]
## Formula: get(response_variable) ~ day + patch_type + patch_type:day +
## (day | culture_ID)
## Data: filtered_data
## Control: lmerControl(optimizer = "Nelder_Mead")
##
## AIC BIC logLik deviance df.resid
## 179.0 199.0 -81.5 163.0 82
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.7 -0.7 0.0 0.6 3.6
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## culture_ID (Intercept) 3e-01 0.51
## day 5e-04 0.02 -1.00
## Residual 3e-01 0.57
## Number of obs: 90, groups: culture_ID, 15
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 3.92 0.37 16.09 10.5
## day -0.15 0.02 21.17 -8.3
## patch_typeSmall connected to small -0.33 0.46 16.09 -0.7
## day:patch_typeSmall connected to small 0.01 0.02 21.17 0.7
## Pr(>|t|)
## (Intercept) 1e-08 ***
## day 4e-08 ***
## patch_typeSmall connected to small 0.5
## day:patch_typeSmall connected to small 0.5
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) day p_Scts
## day -0.952
## ptch_tyScts -0.816 0.777
## dy:ptc_Scts 0.777 -0.816 -0.952
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')Click for model ANOVA
anova(full_model)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## day 54.390 54.390 1 21.167 165.4017 1.797e-11 ***
## patch_type 0.167 0.167 1 16.090 0.5089 0.4859
## day:patch_type 0.147 0.147 1 21.167 0.4457 0.5116
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#Create resid vs fitted (if your number of fitted values is lower than the number of rows in the filtered dataset, it's probably because you have NAs)
res_vs_fit = filtered_data %>%
mutate(predicted = fitted(full_model),
residuals = resid(full_model)) %>%
plot_ly(x = ~predicted,
y = ~residuals,
type = "scatter",
mode = "markers",
marker = list(size = 5, color = "#4C78A8"),
text = paste(" System nr: ", filtered_data$culture_ID, "<br>",
"Day: ", filtered_data$day, "<br>",
"Meta-ecosystem: ", filtered_data$patch_type, "<br>",
"Tot Biomass: ", round(filtered_data$bioarea_mm2_per_ml, digits = 2), "<br>"),
hoverinfo = "text") %>%
plotly::layout(
xaxis = list(title = "Fitted Values"),
yaxis = list(title = "Residuals")
)Click for residual plots
res_vs_fitqqnorm(resid(full_model))model_stats_full = compute.model.stats(full_model,
null_model,
"mixed_model")
model_stats_full %>%
mutate(deltaAIC = round(deltaAIC, digits = 1),
p_value = round(p_value, digits = 3),
R2 = NULL,
evidence = "",
evidence = ifelse(p_value > 0.1,
"none",
evidence),
evidence = ifelse(p_value < 0.1,
"* weak",
evidence),
evidence = ifelse(p_value < 0.05,
"** moderate",
evidence),
evidence = ifelse(p_value < 0.01,
"*** strong",
evidence),
evidence = ifelse(p_value < 0.001,
"**** very strong",
evidence),
p_value = ifelse(p_value < 0.001,
"< 0.001",
p_value)) %>%
print()## deltaAIC p_value evidence
## 1 3.5 0.778 none
patch_type)
reduced_model = lmer(
get(response_variable) ~
day +
patch_type +
(day | culture_ID),
data = filtered_data,
REML = FALSE,
control = lmerControl(optimizer = "Nelder_Mead")
)Click for model output
print(summary(reduced_model), digits = 1)## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
## method [lmerModLmerTest]
## Formula: get(response_variable) ~ day + patch_type + (day | culture_ID)
## Data: filtered_data
## Control: lmerControl(optimizer = "Nelder_Mead")
##
## AIC BIC logLik deviance df.resid
## 177.5 195.0 -81.7 163.5 83
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.6 -0.7 0.0 0.6 3.7
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## culture_ID (Intercept) 3e-01 0.53
## day 5e-04 0.02 -1.00
## Residual 3e-01 0.57
## Number of obs: 90, groups: culture_ID, 15
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.73 0.24 20.52 15.7 7e-13
## day -0.14 0.01 20.57 -13.3 2e-11
## patch_typeSmall connected to small -0.04 0.14 41.57 -0.2 0.8
##
## (Intercept) ***
## day ***
## patch_typeSmall connected to small
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) day
## day -0.876
## ptch_tyScts -0.393 0.000
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')#Create resid vs fitted
res_vs_fit = filtered_data %>%
mutate(predicted = fitted(reduced_model),
residuals = resid(reduced_model)) %>%
plot_ly(x = ~predicted,
y = ~residuals,
type = "scatter",
mode = "markers",
marker = list(size = 5, color = "#4C78A8"),
text = paste(" System nr: ", filtered_data$culture_ID, "<br>",
"Day: ", filtered_data$day, "<br>",
"Meta-ecosystem: ", filtered_data$patch_type, "<br>",
"Tot Biomass: ", round(filtered_data$bioarea_mm2_per_ml, digits = 2), "<br>"),
hoverinfo = "text") %>%
plotly::layout(
xaxis = list(title = "Fitted Values"),
yaxis = list(title = "Residuals")
)Click for residual plots
res_vs_fitqqnorm(resid(reduced_model))model_stats_fixed = compute.model.stats(reduced_model,
null_model,
"mixed_model")
model_stats_fixed %>%
mutate(deltaAIC = round(deltaAIC, digits = 1),
p_value = round(p_value, digits = 3),
R2 = NULL,
evidence = "",
evidence = ifelse(p_value > 0.1,
"none",
evidence),
evidence = ifelse(p_value < 0.1,
"* weak",
evidence),
evidence = ifelse(p_value < 0.05,
"** moderate",
evidence),
evidence = ifelse(p_value < 0.01,
"*** strong",
evidence),
evidence = ifelse(p_value < 0.001,
"**** very strong",
evidence),
p_value = ifelse(p_value < 0.001,
"< 0.001",
p_value)) %>%
print()## deltaAIC p_value evidence
## 1 1.9 0.805 noneresults_table = fill.results.table(
results_table,
response_variable,
patch_type_i,
model_stats_full,
model_stats_fixed
)Shannon
response_variable = "shannon"filtered_data = ds_patches %>%
filter(time_point >= first_time_point_model,
time_point <= last_time_point_model,
patch_type %in% patch_type_i,
!is.na(!!sym(response_variable)))plot.patches.points(filtered_data,
patch_type_i,
response_variable) 
null_model = lmer(
get(response_variable) ~
day +
(day | culture_ID),
data = filtered_data,
REML = FALSE,
control = lmerControl(optimizer = "Nelder_Mead")
)patch_type and
patch_type : day)
full_model = lmer(
get(response_variable) ~
day +
patch_type +
patch_type : day +
(day | culture_ID),
data = filtered_data,
REML = FALSE,
control = lmerControl(optimizer = "Nelder_Mead")
)Click for model output
print(summary(full_model), digits = 1)## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
## method [lmerModLmerTest]
## Formula: get(response_variable) ~ day + patch_type + patch_type:day +
## (day | culture_ID)
## Data: filtered_data
## Control: lmerControl(optimizer = "Nelder_Mead")
##
## AIC BIC logLik deviance df.resid
## 89.5 109.4 -36.7 73.5 81
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.86 -0.55 0.06 0.58 2.40
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## culture_ID (Intercept) 1e-02 0.11
## day 1e-04 0.01 -1.00
## Residual 1e-01 0.35
## Number of obs: 89, groups: culture_ID, 15
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 1.787 0.193 47.907 9.3
## day -0.048 0.011 26.275 -4.4
## patch_typeSmall connected to small -0.045 0.234 47.492 -0.2
## day:patch_typeSmall connected to small 0.004 0.013 25.695 0.3
## Pr(>|t|)
## (Intercept) 3e-12 ***
## day 2e-04 ***
## patch_typeSmall connected to small 0.8
## day:patch_typeSmall connected to small 0.7
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) day p_Scts
## day -0.928
## ptch_tyScts -0.822 0.763
## dy:ptc_Scts 0.767 -0.826 -0.927
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')Click for model ANOVA
anova(full_model)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## day 5.9530 5.9530 1 25.695 47.6357 2.674e-07 ***
## patch_type 0.0046 0.0046 1 47.492 0.0370 0.8484
## day:patch_type 0.0133 0.0133 1 25.695 0.1067 0.7465
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#Create resid vs fitted (if your number of fitted values is lower than the number of rows in the filtered dataset, it's probably because you have NAs)
res_vs_fit = filtered_data %>%
mutate(predicted = fitted(full_model),
residuals = resid(full_model)) %>%
plot_ly(x = ~predicted,
y = ~residuals,
type = "scatter",
mode = "markers",
marker = list(size = 5, color = "#4C78A8"),
text = paste(" System nr: ", filtered_data$culture_ID, "<br>",
"Day: ", filtered_data$day, "<br>",
"Meta-ecosystem: ", filtered_data$patch_type, "<br>",
"Tot Biomass: ", round(filtered_data$bioarea_mm2_per_ml, digits = 2), "<br>"),
hoverinfo = "text") %>%
plotly::layout(
xaxis = list(title = "Fitted Values"),
yaxis = list(title = "Residuals")
)Click for residual plots
res_vs_fitqqnorm(resid(full_model))
model_stats_full = compute.model.stats(full_model,
null_model,
"mixed_model")
model_stats_full %>%
mutate(deltaAIC = round(deltaAIC, digits = 1),
p_value = round(p_value, digits = 3),
R2 = NULL,
evidence = "",
evidence = ifelse(p_value > 0.1,
"none",
evidence),
evidence = ifelse(p_value < 0.1,
"* weak",
evidence),
evidence = ifelse(p_value < 0.05,
"** moderate",
evidence),
evidence = ifelse(p_value < 0.01,
"*** strong",
evidence),
evidence = ifelse(p_value < 0.001,
"**** very strong",
evidence),
p_value = ifelse(p_value < 0.001,
"< 0.001",
p_value)) %>%
print()## deltaAIC p_value evidence
## 1 3.8 0.907 none
patch_type)
reduced_model = lmer(
get(response_variable) ~
day +
patch_type +
(day | culture_ID),
data = filtered_data,
REML = FALSE,
control = lmerControl(optimizer = "Nelder_Mead")
)Click for model output
print(summary(reduced_model), digits = 1)## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
## method [lmerModLmerTest]
## Formula: get(response_variable) ~ day + patch_type + (day | culture_ID)
## Data: filtered_data
## Control: lmerControl(optimizer = "Nelder_Mead")
##
## AIC BIC logLik deviance df.resid
## 87.6 105.0 -36.8 73.6 82
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.86 -0.57 0.08 0.65 2.33
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## culture_ID (Intercept) 1e-02 0.11
## day 1e-04 0.01 -1.00
## Residual 1e-01 0.35
## Number of obs: 89, groups: culture_ID, 15
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.738 0.124 67.649 14.0 <2e-16
## day -0.045 0.006 24.893 -7.3 1e-07
## patch_typeSmall connected to small 0.026 0.087 33.116 0.3 0.8
##
## (Intercept) ***
## day ***
## patch_typeSmall connected to small
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) day
## day -0.815
## ptch_tyScts -0.461 -0.016
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')#Create resid vs fitted
res_vs_fit = filtered_data %>%
mutate(predicted = fitted(reduced_model),
residuals = resid(reduced_model)) %>%
plot_ly(x = ~predicted,
y = ~residuals,
type = "scatter",
mode = "markers",
marker = list(size = 5, color = "#4C78A8"),
text = paste(" System nr: ", filtered_data$culture_ID, "<br>",
"Day: ", filtered_data$day, "<br>",
"Meta-ecosystem: ", filtered_data$patch_type, "<br>",
"Tot Biomass: ", round(filtered_data$bioarea_mm2_per_ml, digits = 2), "<br>"),
hoverinfo = "text") %>%
plotly::layout(
xaxis = list(title = "Fitted Values"),
yaxis = list(title = "Residuals")
)Click for residual plots
res_vs_fitqqnorm(resid(reduced_model))
model_stats_fixed = compute.model.stats(reduced_model,
null_model,
"mixed_model")
model_stats_fixed %>%
mutate(deltaAIC = round(deltaAIC, digits = 1),
p_value = round(p_value, digits = 3),
R2 = NULL,
evidence = "",
evidence = ifelse(p_value > 0.1,
"none",
evidence),
evidence = ifelse(p_value < 0.1,
"* weak",
evidence),
evidence = ifelse(p_value < 0.05,
"** moderate",
evidence),
evidence = ifelse(p_value < 0.01,
"*** strong",
evidence),
evidence = ifelse(p_value < 0.001,
"**** very strong",
evidence),
p_value = ifelse(p_value < 0.001,
"< 0.001",
p_value)) %>%
print()## deltaAIC p_value evidence
## 1 1.9 0.767 noneresults_table = fill.results.table(
results_table,
response_variable,
patch_type_i,
model_stats_full,
model_stats_fixed
)Richness
response_variable = "species_richness"filtered_data = ds_patches %>%
filter(time_point >= first_time_point_model,
time_point <= last_time_point_model,
patch_type %in% patch_type_i,
!is.na(!!sym(response_variable)))plot.patches.points(filtered_data,
patch_type_i,
response_variable) 
null_model = lmer(
get(response_variable) ~
day +
(day | culture_ID),
data = filtered_data,
REML = FALSE,
control = lmerControl(optimizer = "Nelder_Mead")
)patch_type and
patch_type : day)
full_model = lmer(
get(response_variable) ~
day +
patch_type +
patch_type : day +
(day | culture_ID),
data = filtered_data,
REML = FALSE,
control = lmerControl(optimizer = "Nelder_Mead")
)Click for model output
print(summary(full_model), digits = 1)## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
## method [lmerModLmerTest]
## Formula: get(response_variable) ~ day + patch_type + patch_type:day +
## (day | culture_ID)
## Data: filtered_data
## Control: lmerControl(optimizer = "Nelder_Mead")
##
## AIC BIC logLik deviance df.resid
## 333.5 353.5 -158.7 317.5 82
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.3 -0.8 -0.2 0.5 2.7
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## culture_ID (Intercept) 9e-11 1e-05
## day 4e-13 7e-07 -1.00
## Residual 2e+00 1e+00
## Number of obs: 90, groups: culture_ID, 15
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 8.32 0.73 90.00 11.4
## day -0.25 0.04 90.00 -6.7
## patch_typeSmall connected to small -0.63 0.89 90.00 -0.7
## day:patch_typeSmall connected to small 0.04 0.05 90.00 0.8
## Pr(>|t|)
## (Intercept) <2e-16 ***
## day 2e-09 ***
## patch_typeSmall connected to small 0.5
## day:patch_typeSmall connected to small 0.4
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) day p_Scts
## day -0.935
## ptch_tyScts -0.816 0.763
## dy:ptc_Scts 0.763 -0.816 -0.935
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')Click for model ANOVA
anova(full_model)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## day 204.298 204.298 1 90 102.4855 <2e-16 ***
## patch_type 1.004 1.004 1 90 0.5036 0.4797
## day:patch_type 1.338 1.338 1 90 0.6710 0.4149
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#Create resid vs fitted (if your number of fitted values is lower than the number of rows in the filtered dataset, it's probably because you have NAs)
res_vs_fit = filtered_data %>%
mutate(predicted = fitted(full_model),
residuals = resid(full_model)) %>%
plot_ly(x = ~predicted,
y = ~residuals,
type = "scatter",
mode = "markers",
marker = list(size = 5, color = "#4C78A8"),
text = paste(" System nr: ", filtered_data$culture_ID, "<br>",
"Day: ", filtered_data$day, "<br>",
"Meta-ecosystem: ", filtered_data$patch_type, "<br>",
"Tot Biomass: ", round(filtered_data$bioarea_mm2_per_ml, digits = 2), "<br>"),
hoverinfo = "text") %>%
plotly::layout(
xaxis = list(title = "Fitted Values"),
yaxis = list(title = "Residuals")
)Click for residual plots
res_vs_fitqqnorm(resid(full_model))
model_stats_full = compute.model.stats(full_model,
null_model,
"mixed_model")
model_stats_full %>%
mutate(deltaAIC = round(deltaAIC, digits = 1),
p_value = round(p_value, digits = 3),
R2 = NULL,
evidence = "",
evidence = ifelse(p_value > 0.1,
"none",
evidence),
evidence = ifelse(p_value < 0.1,
"* weak",
evidence),
evidence = ifelse(p_value < 0.05,
"** moderate",
evidence),
evidence = ifelse(p_value < 0.01,
"*** strong",
evidence),
evidence = ifelse(p_value < 0.001,
"**** very strong",
evidence),
p_value = ifelse(p_value < 0.001,
"< 0.001",
p_value)) %>%
print()## deltaAIC p_value evidence
## 1 3.3 0.707 none
patch_type)
reduced_model = lmer(
get(response_variable) ~
day +
patch_type +
(day | culture_ID),
data = filtered_data,
REML = FALSE,
control = lmerControl(optimizer = "Nelder_Mead")
)Click for model output
print(summary(reduced_model), digits = 1)## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
## method [lmerModLmerTest]
## Formula: get(response_variable) ~ day + patch_type + (day | culture_ID)
## Data: filtered_data
## Control: lmerControl(optimizer = "Nelder_Mead")
##
## AIC BIC logLik deviance df.resid
## 332.2 349.7 -159.1 318.2 83
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.3 -0.8 -0.1 0.6 2.5
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## culture_ID (Intercept) 9e-10 3e-05
## day 5e-12 2e-06 -1.00
## Residual 2e+00 1e+00
## Number of obs: 90, groups: culture_ID, 15
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 7.86 0.47 90.00 16.7 <2e-16
## day -0.23 0.02 90.00 -10.4 <2e-16
## patch_typeSmall connected to small 0.05 0.32 90.00 0.2 0.9
##
## (Intercept) ***
## day ***
## patch_typeSmall connected to small
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) day
## day -0.836
## ptch_tyScts -0.448 0.000
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')#Create resid vs fitted
res_vs_fit = filtered_data %>%
mutate(predicted = fitted(reduced_model),
residuals = resid(reduced_model)) %>%
plot_ly(x = ~predicted,
y = ~residuals,
type = "scatter",
mode = "markers",
marker = list(size = 5, color = "#4C78A8"),
text = paste(" System nr: ", filtered_data$culture_ID, "<br>",
"Day: ", filtered_data$day, "<br>",
"Meta-ecosystem: ", filtered_data$patch_type, "<br>",
"Tot Biomass: ", round(filtered_data$bioarea_mm2_per_ml, digits = 2), "<br>"),
hoverinfo = "text") %>%
plotly::layout(
xaxis = list(title = "Fitted Values"),
yaxis = list(title = "Residuals")
)Click for residual plots
res_vs_fitqqnorm(resid(reduced_model))
model_stats_fixed = compute.model.stats(reduced_model,
null_model,
"mixed_model")
model_stats_fixed %>%
mutate(deltaAIC = round(deltaAIC, digits = 1),
p_value = round(p_value, digits = 3),
R2 = NULL,
evidence = "",
evidence = ifelse(p_value > 0.1,
"none",
evidence),
evidence = ifelse(p_value < 0.1,
"* weak",
evidence),
evidence = ifelse(p_value < 0.05,
"** moderate",
evidence),
evidence = ifelse(p_value < 0.01,
"*** strong",
evidence),
evidence = ifelse(p_value < 0.001,
"**** very strong",
evidence),
p_value = ifelse(p_value < 0.001,
"< 0.001",
p_value)) %>%
print()## deltaAIC p_value evidence
## 1 2 0.875 noneresults_table = fill.results.table(
results_table,
response_variable,
patch_type_i,
model_stats_full,
model_stats_fixed
)Evenness
response_variable = "evenness_pielou"filtered_data = ds_patches %>%
filter(time_point >= first_time_point_model,
time_point <= last_time_point_model,
patch_type %in% patch_type_i,
!is.na(!!sym(response_variable)))plot.patches.points(filtered_data,
patch_type_i,
response_variable) 
null_model = lmer(
get(response_variable) ~
day +
(day | culture_ID),
data = filtered_data,
REML = FALSE,
control = lmerControl(optimizer = "Nelder_Mead")
)patch_type and
patch_type : day)
full_model = lmer(
get(response_variable) ~
day +
patch_type +
patch_type : day +
(day | culture_ID),
data = filtered_data,
REML = FALSE,
control = lmerControl(optimizer = "Nelder_Mead")
)Click for model output
print(summary(full_model), digits = 1)## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
## method [lmerModLmerTest]
## Formula: get(response_variable) ~ day + patch_type + patch_type:day +
## (day | culture_ID)
## Data: filtered_data
## Control: lmerControl(optimizer = "Nelder_Mead")
##
## AIC BIC logLik deviance df.resid
## -68.5 -49.3 42.2 -84.5 73
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.7 -0.3 0.0 0.6 2.0
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## culture_ID (Intercept) 7e-04 0.027
## day 8e-06 0.003 -1.00
## Residual 2e-02 0.141
## Number of obs: 81, groups: culture_ID, 15
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 0.870 0.076 61.647 12
## day -0.005 0.004 29.903 -1
## patch_typeSmall connected to small -0.128 0.093 61.775 -1
## day:patch_typeSmall connected to small 0.009 0.005 30.919 2
## Pr(>|t|)
## (Intercept) <2e-16 ***
## day 0.25
## patch_typeSmall connected to small 0.17
## day:patch_typeSmall connected to small 0.09 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) day p_Scts
## day -0.925
## ptch_tyScts -0.815 0.753
## dy:ptc_Scts 0.755 -0.816 -0.925
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')Click for model ANOVA
anova(full_model)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## day 0.000460 0.000460 1 30.919 0.0231 0.88009
## patch_type 0.037879 0.037879 1 61.775 1.9063 0.17235
## day:patch_type 0.060612 0.060612 1 30.919 3.0505 0.09064 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#Create resid vs fitted (if your number of fitted values is lower than the number of rows in the filtered dataset, it's probably because you have NAs)
res_vs_fit = filtered_data %>%
mutate(predicted = fitted(full_model),
residuals = resid(full_model)) %>%
plot_ly(x = ~predicted,
y = ~residuals,
type = "scatter",
mode = "markers",
marker = list(size = 5, color = "#4C78A8"),
text = paste(" System nr: ", filtered_data$culture_ID, "<br>",
"Day: ", filtered_data$day, "<br>",
"Meta-ecosystem: ", filtered_data$patch_type, "<br>",
"Tot Biomass: ", round(filtered_data$bioarea_mm2_per_ml, digits = 2), "<br>"),
hoverinfo = "text") %>%
plotly::layout(
xaxis = list(title = "Fitted Values"),
yaxis = list(title = "Residuals")
)Click for residual plots
res_vs_fitqqnorm(resid(full_model))
model_stats_full = compute.model.stats(full_model,
null_model,
"mixed_model")
model_stats_full %>%
mutate(deltaAIC = round(deltaAIC, digits = 1),
p_value = round(p_value, digits = 3),
R2 = NULL,
evidence = "",
evidence = ifelse(p_value > 0.1,
"none",
evidence),
evidence = ifelse(p_value < 0.1,
"* weak",
evidence),
evidence = ifelse(p_value < 0.05,
"** moderate",
evidence),
evidence = ifelse(p_value < 0.01,
"*** strong",
evidence),
evidence = ifelse(p_value < 0.001,
"**** very strong",
evidence),
p_value = ifelse(p_value < 0.001,
"< 0.001",
p_value)) %>%
print()## deltaAIC p_value evidence
## 1 1 0.22 none
patch_type)
reduced_model = lmer(
get(response_variable) ~
day +
patch_type +
(day | culture_ID),
data = filtered_data,
REML = FALSE,
control = lmerControl(optimizer = "Nelder_Mead")
)Click for model output
print(summary(reduced_model), digits = 1)## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
## method [lmerModLmerTest]
## Formula: get(response_variable) ~ day + patch_type + (day | culture_ID)
## Data: filtered_data
## Control: lmerControl(optimizer = "Nelder_Mead")
##
## AIC BIC logLik deviance df.resid
## -67.7 -50.9 40.8 -81.7 74
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.8 -0.4 0.1 0.7 2.0
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## culture_ID (Intercept) 2e-03 0.048
## day 2e-05 0.004 -1.00
## Residual 2e-02 0.141
## Number of obs: 81, groups: culture_ID, 15
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 8e-01 5e-02 6e+01 15.1 <2e-16
## day 1e-03 3e-03 2e+01 0.5 0.6
## patch_typeSmall connected to small 2e-02 4e-02 3e+01 0.5 0.6
##
## (Intercept) ***
## day
## patch_typeSmall connected to small
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) day
## day -0.816
## ptch_tyScts -0.464 -0.010
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')#Create resid vs fitted
res_vs_fit = filtered_data %>%
mutate(predicted = fitted(reduced_model),
residuals = resid(reduced_model)) %>%
plot_ly(x = ~predicted,
y = ~residuals,
type = "scatter",
mode = "markers",
marker = list(size = 5, color = "#4C78A8"),
text = paste(" System nr: ", filtered_data$culture_ID, "<br>",
"Day: ", filtered_data$day, "<br>",
"Meta-ecosystem: ", filtered_data$patch_type, "<br>",
"Tot Biomass: ", round(filtered_data$bioarea_mm2_per_ml, digits = 2), "<br>"),
hoverinfo = "text") %>%
plotly::layout(
xaxis = list(title = "Fitted Values"),
yaxis = list(title = "Residuals")
)Click for residual plots
res_vs_fitqqnorm(resid(reduced_model))
model_stats_fixed = compute.model.stats(reduced_model,
null_model,
"mixed_model")
model_stats_fixed %>%
mutate(deltaAIC = round(deltaAIC, digits = 1),
p_value = round(p_value, digits = 3),
R2 = NULL,
evidence = "",
evidence = ifelse(p_value > 0.1,
"none",
evidence),
evidence = ifelse(p_value < 0.1,
"* weak",
evidence),
evidence = ifelse(p_value < 0.05,
"** moderate",
evidence),
evidence = ifelse(p_value < 0.01,
"*** strong",
evidence),
evidence = ifelse(p_value < 0.001,
"**** very strong",
evidence),
p_value = ifelse(p_value < 0.001,
"< 0.001",
p_value)) %>%
print()## deltaAIC p_value evidence
## 1 1.8 0.662 noneresults_table = fill.results.table(
results_table,
response_variable,
patch_type_i,
model_stats_full,
model_stats_fixed
)Median Size
response_variable = "median_body_area_µm2"filtered_data = ds_patches %>%
filter(time_point >= first_time_point_model,
time_point <= last_time_point_model,
patch_type %in% patch_type_i,
!is.na(!!sym(response_variable)))plot.patches.points(filtered_data,
patch_type_i,
response_variable) 
null_model = lmer(
get(response_variable) ~
day +
(day | culture_ID),
data = filtered_data,
REML = FALSE,
control = lmerControl(optimizer = "Nelder_Mead")
)patch_type and
patch_type : day)
full_model = lmer(
get(response_variable) ~
day +
patch_type +
patch_type : day +
(day | culture_ID),
data = filtered_data,
REML = FALSE,
control = lmerControl(optimizer = "Nelder_Mead")
)Click for model output
print(summary(full_model), digits = 1)## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
## method [lmerModLmerTest]
## Formula: get(response_variable) ~ day + patch_type + patch_type:day +
## (day | culture_ID)
## Data: filtered_data
## Control: lmerControl(optimizer = "Nelder_Mead")
##
## AIC BIC logLik deviance df.resid
## 1420.0 1439.9 -702.0 1404.0 81
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.53 -0.60 -0.08 0.86 1.81
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## culture_ID (Intercept) 1e+05 341
## day 5e+02 23 -1.00
## Residual 4e+05 625
## Number of obs: 89, groups: culture_ID, 15
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 3143 362 24 8.7
## day 24 20 18 1.2
## patch_typeSmall connected to small -84 441 24 -0.2
## day:patch_typeSmall connected to small 12 24 17 0.5
## Pr(>|t|)
## (Intercept) 6e-09 ***
## day 0.3
## patch_typeSmall connected to small 0.9
## day:patch_typeSmall connected to small 0.6
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) day p_Scts
## day -0.945
## ptch_tyScts -0.821 0.776
## dy:ptc_Scts 0.780 -0.825 -0.944
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')Click for model ANOVA
anova(full_model)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## day 2363748 2363748 1 17.341 6.0523 0.02466 *
## patch_type 14117 14117 1 24.108 0.0361 0.85080
## day:patch_type 101258 101258 1 17.341 0.2593 0.61704
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#Create resid vs fitted (if your number of fitted values is lower than the number of rows in the filtered dataset, it's probably because you have NAs)
res_vs_fit = filtered_data %>%
mutate(predicted = fitted(full_model),
residuals = resid(full_model)) %>%
plot_ly(x = ~predicted,
y = ~residuals,
type = "scatter",
mode = "markers",
marker = list(size = 5, color = "#4C78A8"),
text = paste(" System nr: ", filtered_data$culture_ID, "<br>",
"Day: ", filtered_data$day, "<br>",
"Meta-ecosystem: ", filtered_data$patch_type, "<br>",
"Tot Biomass: ", round(filtered_data$bioarea_mm2_per_ml, digits = 2), "<br>"),
hoverinfo = "text") %>%
plotly::layout(
xaxis = list(title = "Fitted Values"),
yaxis = list(title = "Residuals")
)Click for residual plots
res_vs_fitqqnorm(resid(full_model))
model_stats_full = compute.model.stats(full_model,
null_model,
"mixed_model")
model_stats_full %>%
mutate(deltaAIC = round(deltaAIC, digits = 1),
p_value = round(p_value, digits = 3),
R2 = NULL,
evidence = "",
evidence = ifelse(p_value > 0.1,
"none",
evidence),
evidence = ifelse(p_value < 0.1,
"* weak",
evidence),
evidence = ifelse(p_value < 0.05,
"** moderate",
evidence),
evidence = ifelse(p_value < 0.01,
"*** strong",
evidence),
evidence = ifelse(p_value < 0.001,
"**** very strong",
evidence),
p_value = ifelse(p_value < 0.001,
"< 0.001",
p_value)) %>%
print()## deltaAIC p_value evidence
## 1 3 0.599 none
patch_type)
reduced_model = lmer(
get(response_variable) ~
day +
patch_type +
(day | culture_ID),
data = filtered_data,
REML = FALSE,
control = lmerControl(optimizer = "Nelder_Mead")
)Click for model output
print(summary(reduced_model), digits = 1)## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
## method [lmerModLmerTest]
## Formula: get(response_variable) ~ day + patch_type + (day | culture_ID)
## Data: filtered_data
## Control: lmerControl(optimizer = "Nelder_Mead")
##
## AIC BIC logLik deviance df.resid
## 1418.2 1435.6 -702.1 1404.2 82
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.47 -0.57 -0.07 0.85 1.83
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## culture_ID (Intercept) 1e+05 347
## day 5e+02 23 -1.00
## Residual 4e+05 626
## Number of obs: 89, groups: culture_ID, 15
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2999 228 34 13.2 7e-15
## day 32 12 17 2.8 0.01
## patch_typeSmall connected to small 128 145 56 0.9 0.38
##
## (Intercept) ***
## day *
## patch_typeSmall connected to small
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) day
## day -0.853
## ptch_tyScts -0.412 -0.019
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')#Create resid vs fitted
res_vs_fit = filtered_data %>%
mutate(predicted = fitted(reduced_model),
residuals = resid(reduced_model)) %>%
plot_ly(x = ~predicted,
y = ~residuals,
type = "scatter",
mode = "markers",
marker = list(size = 5, color = "#4C78A8"),
text = paste(" System nr: ", filtered_data$culture_ID, "<br>",
"Day: ", filtered_data$day, "<br>",
"Meta-ecosystem: ", filtered_data$patch_type, "<br>",
"Tot Biomass: ", round(filtered_data$bioarea_mm2_per_ml, digits = 2), "<br>"),
hoverinfo = "text") %>%
plotly::layout(
xaxis = list(title = "Fitted Values"),
yaxis = list(title = "Residuals")
)Click for residual plots
res_vs_fitqqnorm(resid(reduced_model))
model_stats_fixed = compute.model.stats(reduced_model,
null_model,
"mixed_model")
model_stats_fixed %>%
mutate(deltaAIC = round(deltaAIC, digits = 1),
p_value = round(p_value, digits = 3),
R2 = NULL,
evidence = "",
evidence = ifelse(p_value > 0.1,
"none",
evidence),
evidence = ifelse(p_value < 0.1,
"* weak",
evidence),
evidence = ifelse(p_value < 0.05,
"** moderate",
evidence),
evidence = ifelse(p_value < 0.01,
"*** strong",
evidence),
evidence = ifelse(p_value < 0.001,
"**** very strong",
evidence),
p_value = ifelse(p_value < 0.001,
"< 0.001",
p_value)) %>%
print()## deltaAIC p_value evidence
## 1 1.2 0.381 noneresults_table = fill.results.table(
results_table,
response_variable,
patch_type_i,
model_stats_full,
model_stats_fixed
)Populations
for(patch_type_i in patch_type_i){
print(patch_type_i)
p = ds_patches %>%
filter(patch_type == patch_type_i) %>%
group_by(day) %>%
summarise(
Ble = mean(Ble_indiv_per_ml_dominance, na.rm = TRUE),
# Ble_se = sd(Ble_indiv_per_ml_dominance, na.rm = TRUE) / sqrt(n()),
# Ble_ci = qt(c(0.025, 0.975), df = n() - 1) * Ble_se,
Cep = mean(Cep_indiv_per_ml_dominance, na.rm = TRUE),
Col = mean(Col_indiv_per_ml_dominance, na.rm = TRUE),
Eug = mean(Eug_indiv_per_ml_dominance, na.rm = TRUE),
Eup = mean(Eup_indiv_per_ml_dominance, na.rm = TRUE),
Lox = mean(Lox_indiv_per_ml_dominance, na.rm = TRUE),
Pau = mean(Pau_indiv_per_ml_dominance, na.rm = TRUE),
Pca = mean(Pca_indiv_per_ml_dominance, na.rm = TRUE),
Spi = mean(Spi_indiv_per_ml_dominance, na.rm = TRUE),
Spi_te = mean(Spi_te_indiv_per_ml_dominance, na.rm = TRUE),
Tet = mean(Tet_indiv_per_ml_dominance, na.rm = TRUE)
) %>%
pivot_longer(Ble:Tet, names_to = "species", values_to = "species_indiv_per_ml") %>%
ggplot(aes(
x = day,
y = species_indiv_per_ml,
group = interaction(day, species),
color = species
)) +
geom_point(position = position_dodge(dodging),
size = treatment_points_size) +
geom_line(
aes(group = species),
position = position_dodge(dodging),
linewidth = treatment_lines_linewidth) +
labs(x = axis_names %>%
filter(variable == "day") %>%
pull(axis_name),
y = axis_names %>%
filter(variable == "dominance") %>%
pull(axis_name)) +
coord_cartesian(ylim = c(0, 100))
print(p)
}## [1] "Small connected to small"
## [1] "Small isolated"
Medium patches
patch_type_i = c("Medium isolated",
"Medium connected to medium")Biomass
response_variable = "bioarea_mm2_per_ml"filtered_data = ds_patches %>%
filter(time_point >= first_time_point_model,
time_point <= last_time_point_model,
patch_type %in% patch_type_i,
!is.na(!!sym(response_variable)))plot.patches.points(filtered_data,
patch_type_i,
response_variable) 
null_model = lmer(
get(response_variable) ~
day +
(day | culture_ID),
data = filtered_data,
REML = FALSE,
control = lmerControl(optimizer = "Nelder_Mead")
)patch_type and
patch_type : day)
full_model = lmer(
get(response_variable) ~
day +
patch_type +
patch_type : day +
(day | culture_ID),
data = filtered_data,
REML = FALSE,
control = lmerControl(optimizer = "Nelder_Mead")
)Click for model output
print(summary(full_model), digits = 1)## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
## method [lmerModLmerTest]
## Formula: get(response_variable) ~ day + patch_type + patch_type:day +
## (day | culture_ID)
## Data: filtered_data
## Control: lmerControl(optimizer = "Nelder_Mead")
##
## AIC BIC logLik deviance df.resid
## 281.6 301.6 -132.8 265.6 82
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.37 -0.65 -0.05 0.66 2.10
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## culture_ID (Intercept) 3e-01 0.51
## day 3e-04 0.02 -1.00
## Residual 1e+00 1.04
## Number of obs: 90, groups: culture_ID, 15
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 5.119 0.580 17.638 8.8
## day -0.170 0.029 25.546 -5.9
## patch_typeMedium connected to medium 0.915 0.711 17.638 1.3
## day:patch_typeMedium connected to medium -0.004 0.035 25.546 -0.1
## Pr(>|t|)
## (Intercept) 7e-08 ***
## day 4e-06 ***
## patch_typeMedium connected to medium 0.2
## day:patch_typeMedium connected to medium 0.9
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) day p_Mctm
## day -0.936
## ptch_tyMctm -0.816 0.765
## dy:ptc_Mctm 0.765 -0.816 -0.936
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')Click for model ANOVA
anova(full_model)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## day 101.588 101.588 1 25.546 94.2646 4.667e-10 ***
## patch_type 1.787 1.787 1 17.638 1.6580 0.2145
## day:patch_type 0.015 0.015 1 25.546 0.0135 0.9084
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#Create resid vs fitted (if your number of fitted values is lower than the number of rows in the filtered dataset, it's probably because you have NAs)
res_vs_fit = filtered_data %>%
mutate(predicted = fitted(full_model),
residuals = resid(full_model)) %>%
plot_ly(x = ~predicted,
y = ~residuals,
type = "scatter",
mode = "markers",
marker = list(size = 5, color = "#4C78A8"),
text = paste(" System nr: ", filtered_data$culture_ID, "<br>",
"Day: ", filtered_data$day, "<br>",
"Meta-ecosystem: ", filtered_data$patch_type, "<br>",
"Tot Biomass: ", round(filtered_data$bioarea_mm2_per_ml, digits = 2), "<br>"),
hoverinfo = "text") %>%
plotly::layout(
xaxis = list(title = "Fitted Values"),
yaxis = list(title = "Residuals")
)Click for residual plots
res_vs_fitqqnorm(resid(full_model))
model_stats_full = compute.model.stats(full_model,
null_model,
"mixed_model")
model_stats_full %>%
mutate(deltaAIC = round(deltaAIC, digits = 1),
p_value = round(p_value, digits = 3),
R2 = NULL,
evidence = "",
evidence = ifelse(p_value > 0.1,
"none",
evidence),
evidence = ifelse(p_value < 0.1,
"* weak",
evidence),
evidence = ifelse(p_value < 0.05,
"** moderate",
evidence),
evidence = ifelse(p_value < 0.01,
"*** strong",
evidence),
evidence = ifelse(p_value < 0.001,
"**** very strong",
evidence),
p_value = ifelse(p_value < 0.001,
"< 0.001",
p_value)) %>%
print()## deltaAIC p_value evidence
## 1 -4.6 0.014 ** moderate
patch_type)
reduced_model = lmer(
get(response_variable) ~
day +
patch_type +
(day | culture_ID),
data = filtered_data,
REML = FALSE,
control = lmerControl(optimizer = "Nelder_Mead")
)Click for model output
print(summary(reduced_model), digits = 1)## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
## method [lmerModLmerTest]
## Formula: get(response_variable) ~ day + patch_type + (day | culture_ID)
## Data: filtered_data
## Control: lmerControl(optimizer = "Nelder_Mead")
##
## AIC BIC logLik deviance df.resid
## 279.6 297.1 -132.8 265.6 83
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.37 -0.66 -0.05 0.65 2.11
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## culture_ID (Intercept) 3e-01 0.51
## day 3e-04 0.02 -1.00
## Residual 1e+00 1.04
## Number of obs: 90, groups: culture_ID, 15
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 5.17 0.37 23.97 14 7e-13
## day -0.17 0.02 25.46 -10 1e-10
## patch_typeMedium connected to medium 0.84 0.25 20.02 3 0.003
##
## (Intercept) ***
## day ***
## patch_typeMedium connected to medium **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) day
## day -0.839
## ptch_tyMctm -0.445 0.000
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')#Create resid vs fitted
res_vs_fit = filtered_data %>%
mutate(predicted = fitted(reduced_model),
residuals = resid(reduced_model)) %>%
plot_ly(x = ~predicted,
y = ~residuals,
type = "scatter",
mode = "markers",
marker = list(size = 5, color = "#4C78A8"),
text = paste(" System nr: ", filtered_data$culture_ID, "<br>",
"Day: ", filtered_data$day, "<br>",
"Meta-ecosystem: ", filtered_data$patch_type, "<br>",
"Tot Biomass: ", round(filtered_data$bioarea_mm2_per_ml, digits = 2), "<br>"),
hoverinfo = "text") %>%
plotly::layout(
xaxis = list(title = "Fitted Values"),
yaxis = list(title = "Residuals")
)Click for residual plots
res_vs_fitqqnorm(resid(reduced_model))
model_stats_fixed = compute.model.stats(reduced_model,
null_model,
"mixed_model")
model_stats_fixed %>%
mutate(deltaAIC = round(deltaAIC, digits = 1),
p_value = round(p_value, digits = 3),
R2 = NULL,
evidence = "",
evidence = ifelse(p_value > 0.1,
"none",
evidence),
evidence = ifelse(p_value < 0.1,
"* weak",
evidence),
evidence = ifelse(p_value < 0.05,
"** moderate",
evidence),
evidence = ifelse(p_value < 0.01,
"*** strong",
evidence),
evidence = ifelse(p_value < 0.001,
"**** very strong",
evidence),
p_value = ifelse(p_value < 0.001,
"< 0.001",
p_value)) %>%
print()## deltaAIC p_value evidence
## 1 -6.6 0.003 *** strongresults_table = fill.results.table(
results_table,
response_variable,
patch_type_i,
model_stats_full,
model_stats_fixed
)Shannon
response_variable = "shannon"filtered_data = ds_patches %>%
filter(time_point >= first_time_point_model,
time_point <= last_time_point_model,
patch_type %in% patch_type_i,
!is.na(!!sym(response_variable)))plot.patches.points(filtered_data,
patch_type_i,
response_variable) 
null_model = lmer(
get(response_variable) ~
day +
(day | culture_ID),
data = filtered_data,
REML = FALSE,
control = lmerControl(optimizer = "Nelder_Mead")
)patch_type and
patch_type : day)
full_model = lmer(
get(response_variable) ~
day +
patch_type +
patch_type : day +
(day | culture_ID),
data = filtered_data,
REML = FALSE,
control = lmerControl(optimizer = "Nelder_Mead")
)Click for model output
print(summary(full_model), digits = 1)## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
## method [lmerModLmerTest]
## Formula: get(response_variable) ~ day + patch_type + patch_type:day +
## (day | culture_ID)
## Data: filtered_data
## Control: lmerControl(optimizer = "Nelder_Mead")
##
## AIC BIC logLik deviance df.resid
## 47.2 67.2 -15.6 31.2 82
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.3 -0.6 0.1 0.7 1.9
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## culture_ID (Intercept) 3e-02 0.183
## day 4e-05 0.006 -1.00
## Residual 8e-02 0.278
## Number of obs: 90, groups: culture_ID, 15
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 1.182 0.165 20.526 7.2
## day 0.009 0.008 34.267 1.1
## patch_typeMedium connected to medium 0.179 0.202 20.526 0.9
## day:patch_typeMedium connected to medium -0.010 0.010 34.267 -1.0
## Pr(>|t|)
## (Intercept) 5e-07 ***
## day 0.3
## patch_typeMedium connected to medium 0.4
## day:patch_typeMedium connected to medium 0.3
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) day p_Mctm
## day -0.936
## ptch_tyMctm -0.816 0.764
## dy:ptc_Mctm 0.764 -0.816 -0.936
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')Click for model ANOVA
anova(full_model)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## day 0.048608 0.048608 1 34.267 0.6280 0.4336
## patch_type 0.060641 0.060641 1 20.526 0.7834 0.3863
## day:patch_type 0.082004 0.082004 1 34.267 1.0594 0.3106#Create resid vs fitted (if your number of fitted values is lower than the number of rows in the filtered dataset, it's probably because you have NAs)
res_vs_fit = filtered_data %>%
mutate(predicted = fitted(full_model),
residuals = resid(full_model)) %>%
plot_ly(x = ~predicted,
y = ~residuals,
type = "scatter",
mode = "markers",
marker = list(size = 5, color = "#4C78A8"),
text = paste(" System nr: ", filtered_data$culture_ID, "<br>",
"Day: ", filtered_data$day, "<br>",
"Meta-ecosystem: ", filtered_data$patch_type, "<br>",
"Tot Biomass: ", round(filtered_data$bioarea_mm2_per_ml, digits = 2), "<br>"),
hoverinfo = "text") %>%
plotly::layout(
xaxis = list(title = "Fitted Values"),
yaxis = list(title = "Residuals")
)Click for residual plots
res_vs_fitqqnorm(resid(full_model))
model_stats_full = compute.model.stats(full_model,
null_model,
"mixed_model")
model_stats_full %>%
mutate(deltaAIC = round(deltaAIC, digits = 1),
p_value = round(p_value, digits = 3),
R2 = NULL,
evidence = "",
evidence = ifelse(p_value > 0.1,
"none",
evidence),
evidence = ifelse(p_value < 0.1,
"* weak",
evidence),
evidence = ifelse(p_value < 0.05,
"** moderate",
evidence),
evidence = ifelse(p_value < 0.01,
"*** strong",
evidence),
evidence = ifelse(p_value < 0.001,
"**** very strong",
evidence),
p_value = ifelse(p_value < 0.001,
"< 0.001",
p_value)) %>%
print()## deltaAIC p_value evidence
## 1 2.9 0.578 none
patch_type)
reduced_model = lmer(
get(response_variable) ~
day +
patch_type +
(day | culture_ID),
data = filtered_data,
REML = FALSE,
control = lmerControl(optimizer = "Nelder_Mead")
)Click for model output
print(summary(reduced_model), digits = 1)## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
## method [lmerModLmerTest]
## Formula: get(response_variable) ~ day + patch_type + (day | culture_ID)
## Data: filtered_data
## Control: lmerControl(optimizer = "Nelder_Mead")
##
## AIC BIC logLik deviance df.resid
## 46.2 63.7 -16.1 32.2 83
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.4 -0.6 0.2 0.7 1.9
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## culture_ID (Intercept) 4e-02 0.194
## day 5e-05 0.007 -1.00
## Residual 8e-02 0.279
## Number of obs: 90, groups: culture_ID, 15
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 1.313 0.108 24.036 12.2
## day 0.002 0.005 30.559 0.5
## patch_typeMedium connected to medium -0.017 0.071 25.203 -0.2
## Pr(>|t|)
## (Intercept) 9e-12 ***
## day 0.6
## patch_typeMedium connected to medium 0.8
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) day
## day -0.843
## ptch_tyMctm -0.440 0.000
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')#Create resid vs fitted
res_vs_fit = filtered_data %>%
mutate(predicted = fitted(reduced_model),
residuals = resid(reduced_model)) %>%
plot_ly(x = ~predicted,
y = ~residuals,
type = "scatter",
mode = "markers",
marker = list(size = 5, color = "#4C78A8"),
text = paste(" System nr: ", filtered_data$culture_ID, "<br>",
"Day: ", filtered_data$day, "<br>",
"Meta-ecosystem: ", filtered_data$patch_type, "<br>",
"Tot Biomass: ", round(filtered_data$bioarea_mm2_per_ml, digits = 2), "<br>"),
hoverinfo = "text") %>%
plotly::layout(
xaxis = list(title = "Fitted Values"),
yaxis = list(title = "Residuals")
)Click for residual plots
res_vs_fitqqnorm(resid(reduced_model))
model_stats_fixed = compute.model.stats(reduced_model,
null_model,
"mixed_model")
model_stats_fixed %>%
mutate(deltaAIC = round(deltaAIC, digits = 1),
p_value = round(p_value, digits = 3),
R2 = NULL,
evidence = "",
evidence = ifelse(p_value > 0.1,
"none",
evidence),
evidence = ifelse(p_value < 0.1,
"* weak",
evidence),
evidence = ifelse(p_value < 0.05,
"** moderate",
evidence),
evidence = ifelse(p_value < 0.01,
"*** strong",
evidence),
evidence = ifelse(p_value < 0.001,
"**** very strong",
evidence),
p_value = ifelse(p_value < 0.001,
"< 0.001",
p_value)) %>%
print()## deltaAIC p_value evidence
## 1 1.9 0.817 noneresults_table = fill.results.table(
results_table,
response_variable,
patch_type_i,
model_stats_full,
model_stats_fixed
)Richness
response_variable = "species_richness"filtered_data = ds_patches %>%
filter(time_point >= first_time_point_model,
time_point <= last_time_point_model,
patch_type %in% patch_type_i,
!is.na(!!sym(response_variable)))plot.patches.points(filtered_data,
patch_type_i,
response_variable) 
null_model = lmer(
get(response_variable) ~
day +
(day | culture_ID),
data = filtered_data,
REML = FALSE,
control = lmerControl(optimizer = "Nelder_Mead")
)patch_type and
patch_type : day)
full_model = lmer(
get(response_variable) ~
day +
patch_type +
patch_type : day +
(day | culture_ID),
data = filtered_data,
REML = FALSE,
control = lmerControl(optimizer = "Nelder_Mead")
)Click for model output
print(summary(full_model), digits = 1)## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
## method [lmerModLmerTest]
## Formula: get(response_variable) ~ day + patch_type + patch_type:day +
## (day | culture_ID)
## Data: filtered_data
## Control: lmerControl(optimizer = "Nelder_Mead")
##
## AIC BIC logLik deviance df.resid
## 354.3 374.3 -169.1 338.3 82
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.1 -0.6 0.0 0.8 2.5
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## culture_ID (Intercept) 2e-01 0.462
## day 3e-06 0.002 -1.00
## Residual 2e+00 1.534
## Number of obs: 90, groups: culture_ID, 15
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 7.28 0.82 41.14 8.9
## day -0.09 0.04 74.62 -2.1
## patch_typeMedium connected to medium 0.41 1.00 41.14 0.4
## day:patch_typeMedium connected to medium 0.02 0.05 74.62 0.4
## Pr(>|t|)
## (Intercept) 4e-11 ***
## day 0.04 *
## patch_typeMedium connected to medium 0.69
## day:patch_typeMedium connected to medium 0.69
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) day p_Mctm
## day -0.909
## ptch_tyMctm -0.816 0.742
## dy:ptc_Mctm 0.742 -0.816 -0.909
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')Click for model ANOVA
anova(full_model)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## day 21.3935 21.3935 1 74.621 9.0856 0.003519 **
## patch_type 0.3898 0.3898 1 41.136 0.1655 0.686222
## day:patch_type 0.3732 0.3732 1 74.621 0.1585 0.691689
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#Create resid vs fitted (if your number of fitted values is lower than the number of rows in the filtered dataset, it's probably because you have NAs)
res_vs_fit = filtered_data %>%
mutate(predicted = fitted(full_model),
residuals = resid(full_model)) %>%
plot_ly(x = ~predicted,
y = ~residuals,
type = "scatter",
mode = "markers",
marker = list(size = 5, color = "#4C78A8"),
text = paste(" System nr: ", filtered_data$culture_ID, "<br>",
"Day: ", filtered_data$day, "<br>",
"Meta-ecosystem: ", filtered_data$patch_type, "<br>",
"Tot Biomass: ", round(filtered_data$bioarea_mm2_per_ml, digits = 2), "<br>"),
hoverinfo = "text") %>%
plotly::layout(
xaxis = list(title = "Fitted Values"),
yaxis = list(title = "Residuals")
)Click for residual plots
res_vs_fitqqnorm(resid(full_model))
model_stats_full = compute.model.stats(full_model,
null_model,
"mixed_model")
model_stats_full %>%
mutate(deltaAIC = round(deltaAIC, digits = 1),
p_value = round(p_value, digits = 3),
R2 = NULL,
evidence = "",
evidence = ifelse(p_value > 0.1,
"none",
evidence),
evidence = ifelse(p_value < 0.1,
"* weak",
evidence),
evidence = ifelse(p_value < 0.05,
"** moderate",
evidence),
evidence = ifelse(p_value < 0.01,
"*** strong",
evidence),
evidence = ifelse(p_value < 0.001,
"**** very strong",
evidence),
p_value = ifelse(p_value < 0.001,
"< 0.001",
p_value)) %>%
print()## deltaAIC p_value evidence
## 1 0.8 0.2 none
patch_type)
reduced_model = lmer(
get(response_variable) ~
day +
patch_type +
(day | culture_ID),
data = filtered_data,
REML = FALSE,
control = lmerControl(optimizer = "Nelder_Mead")
)Click for model output
print(summary(reduced_model), digits = 1)## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
## method [lmerModLmerTest]
## Formula: get(response_variable) ~ day + patch_type + (day | culture_ID)
## Data: filtered_data
## Control: lmerControl(optimizer = "Nelder_Mead")
##
## AIC BIC logLik deviance df.resid
## 352.4 369.9 -169.2 338.4 83
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.10 -0.61 -0.03 0.73 2.56
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## culture_ID (Intercept) 2e-01 0.461
## day 3e-06 0.002 -1.00
## Residual 2e+00 1.536
## Number of obs: 90, groups: culture_ID, 15
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 7.03 0.55 44.26 13 <2e-16
## day -0.07 0.02 74.60 -3 0.003
## patch_typeMedium connected to medium 0.77 0.42 15.01 2 0.084
##
## (Intercept) ***
## day **
## patch_typeMedium connected to medium .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) day
## day -0.784
## ptch_tyMctm -0.507 0.000
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')#Create resid vs fitted
res_vs_fit = filtered_data %>%
mutate(predicted = fitted(reduced_model),
residuals = resid(reduced_model)) %>%
plot_ly(x = ~predicted,
y = ~residuals,
type = "scatter",
mode = "markers",
marker = list(size = 5, color = "#4C78A8"),
text = paste(" System nr: ", filtered_data$culture_ID, "<br>",
"Day: ", filtered_data$day, "<br>",
"Meta-ecosystem: ", filtered_data$patch_type, "<br>",
"Tot Biomass: ", round(filtered_data$bioarea_mm2_per_ml, digits = 2), "<br>"),
hoverinfo = "text") %>%
plotly::layout(
xaxis = list(title = "Fitted Values"),
yaxis = list(title = "Residuals")
)Click for residual plots
res_vs_fitqqnorm(resid(reduced_model))
model_stats_fixed = compute.model.stats(reduced_model,
null_model,
"mixed_model")
model_stats_fixed %>%
mutate(deltaAIC = round(deltaAIC, digits = 1),
p_value = round(p_value, digits = 3),
R2 = NULL,
evidence = "",
evidence = ifelse(p_value > 0.1,
"none",
evidence),
evidence = ifelse(p_value < 0.1,
"* weak",
evidence),
evidence = ifelse(p_value < 0.05,
"** moderate",
evidence),
evidence = ifelse(p_value < 0.01,
"*** strong",
evidence),
evidence = ifelse(p_value < 0.001,
"**** very strong",
evidence),
p_value = ifelse(p_value < 0.001,
"< 0.001",
p_value)) %>%
print()## deltaAIC p_value evidence
## 1 -1.1 0.08 * weakresults_table = fill.results.table(
results_table,
response_variable,
patch_type_i,
model_stats_full,
model_stats_fixed
)Evenness
response_variable = "evenness_pielou"filtered_data = ds_patches %>%
filter(time_point >= first_time_point_model,
time_point <= last_time_point_model,
patch_type %in% patch_type_i,
!is.na(!!sym(response_variable)))plot.patches.points(filtered_data,
patch_type_i,
response_variable) 
null_model = lmer(
get(response_variable) ~
day +
(day | culture_ID),
data = filtered_data,
REML = FALSE,
control = lmerControl(optimizer = "Nelder_Mead")
)patch_type and
patch_type : day)
full_model = lmer(
get(response_variable) ~
day +
patch_type +
patch_type : day +
(day | culture_ID),
data = filtered_data,
REML = FALSE,
control = lmerControl(optimizer = "Nelder_Mead")
)Click for model output
print(summary(full_model), digits = 1)## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
## method [lmerModLmerTest]
## Formula: get(response_variable) ~ day + patch_type + patch_type:day +
## (day | culture_ID)
## Data: filtered_data
## Control: lmerControl(optimizer = "Nelder_Mead")
##
## AIC BIC logLik deviance df.resid
## -106.3 -86.3 61.2 -122.3 82
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.2 -0.7 0.2 0.8 1.7
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## culture_ID (Intercept) 8e-12 3e-06
## day 2e-14 1e-07 -1.00
## Residual 2e-02 1e-01
## Number of obs: 90, groups: culture_ID, 15
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 0.618 0.063 90.000 9.8
## day 0.010 0.003 90.000 3.1
## patch_typeMedium connected to medium 0.044 0.077 90.000 0.6
## day:patch_typeMedium connected to medium -0.006 0.004 90.000 -1.6
## Pr(>|t|)
## (Intercept) 8e-16 ***
## day 0.002 **
## patch_typeMedium connected to medium 0.573
## day:patch_typeMedium connected to medium 0.113
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) day p_Mctm
## day -0.935
## ptch_tyMctm -0.816 0.763
## dy:ptc_Mctm 0.763 -0.816 -0.935
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')Click for model ANOVA
anova(full_model)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## day 0.184092 0.184092 1 90 12.2383 0.0007298 ***
## patch_type 0.004812 0.004812 1 90 0.3199 0.5730908
## day:patch_type 0.038515 0.038515 1 90 2.5605 0.1130708
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#Create resid vs fitted (if your number of fitted values is lower than the number of rows in the filtered dataset, it's probably because you have NAs)
res_vs_fit = filtered_data %>%
mutate(predicted = fitted(full_model),
residuals = resid(full_model)) %>%
plot_ly(x = ~predicted,
y = ~residuals,
type = "scatter",
mode = "markers",
marker = list(size = 5, color = "#4C78A8"),
text = paste(" System nr: ", filtered_data$culture_ID, "<br>",
"Day: ", filtered_data$day, "<br>",
"Meta-ecosystem: ", filtered_data$patch_type, "<br>",
"Tot Biomass: ", round(filtered_data$bioarea_mm2_per_ml, digits = 2), "<br>"),
hoverinfo = "text") %>%
plotly::layout(
xaxis = list(title = "Fitted Values"),
yaxis = list(title = "Residuals")
)Click for residual plots
res_vs_fitqqnorm(resid(full_model))
model_stats_full = compute.model.stats(full_model,
null_model,
"mixed_model")
model_stats_full %>%
mutate(deltaAIC = round(deltaAIC, digits = 1),
p_value = round(p_value, digits = 3),
R2 = NULL,
evidence = "",
evidence = ifelse(p_value > 0.1,
"none",
evidence),
evidence = ifelse(p_value < 0.1,
"* weak",
evidence),
evidence = ifelse(p_value < 0.05,
"** moderate",
evidence),
evidence = ifelse(p_value < 0.01,
"*** strong",
evidence),
evidence = ifelse(p_value < 0.001,
"**** very strong",
evidence),
p_value = ifelse(p_value < 0.001,
"< 0.001",
p_value)) %>%
print()## deltaAIC p_value evidence
## 1 -5 0.011 ** moderate
patch_type)
reduced_model = lmer(
get(response_variable) ~
day +
patch_type +
(day | culture_ID),
data = filtered_data,
REML = FALSE,
control = lmerControl(optimizer = "Nelder_Mead")
)Click for model output
print(summary(reduced_model), digits = 1)## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
## method [lmerModLmerTest]
## Formula: get(response_variable) ~ day + patch_type + (day | culture_ID)
## Data: filtered_data
## Control: lmerControl(optimizer = "Nelder_Mead")
##
## AIC BIC logLik deviance df.resid
## -105.8 -88.3 59.9 -119.8 83
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.4 -0.7 0.1 0.8 1.6
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## culture_ID (Intercept) 2e-12 1e-06
## day 3e-15 6e-08 -1.00
## Residual 2e-02 1e-01
## Number of obs: 90, groups: culture_ID, 15
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 0.695 0.041 90.000 17
## day 0.006 0.002 90.000 3
## patch_typeMedium connected to medium -0.072 0.028 90.000 -3
## Pr(>|t|)
## (Intercept) <2e-16 ***
## day 0.003 **
## patch_typeMedium connected to medium 0.011 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) day
## day -0.836
## ptch_tyMctm -0.448 0.000
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')#Create resid vs fitted
res_vs_fit = filtered_data %>%
mutate(predicted = fitted(reduced_model),
residuals = resid(reduced_model)) %>%
plot_ly(x = ~predicted,
y = ~residuals,
type = "scatter",
mode = "markers",
marker = list(size = 5, color = "#4C78A8"),
text = paste(" System nr: ", filtered_data$culture_ID, "<br>",
"Day: ", filtered_data$day, "<br>",
"Meta-ecosystem: ", filtered_data$patch_type, "<br>",
"Tot Biomass: ", round(filtered_data$bioarea_mm2_per_ml, digits = 2), "<br>"),
hoverinfo = "text") %>%
plotly::layout(
xaxis = list(title = "Fitted Values"),
yaxis = list(title = "Residuals")
)Click for residual plots
res_vs_fitqqnorm(resid(reduced_model))
model_stats_fixed = compute.model.stats(reduced_model,
null_model,
"mixed_model")
model_stats_fixed %>%
mutate(deltaAIC = round(deltaAIC, digits = 1),
p_value = round(p_value, digits = 3),
R2 = NULL,
evidence = "",
evidence = ifelse(p_value > 0.1,
"none",
evidence),
evidence = ifelse(p_value < 0.1,
"* weak",
evidence),
evidence = ifelse(p_value < 0.05,
"** moderate",
evidence),
evidence = ifelse(p_value < 0.01,
"*** strong",
evidence),
evidence = ifelse(p_value < 0.001,
"**** very strong",
evidence),
p_value = ifelse(p_value < 0.001,
"< 0.001",
p_value)) %>%
print()## deltaAIC p_value evidence
## 1 -4.4 0.011 ** moderateresults_table = fill.results.table(
results_table,
response_variable,
patch_type_i,
model_stats_full,
model_stats_fixed
)Median Size
response_variable = "median_body_area_µm2"filtered_data = ds_patches %>%
filter(time_point >= first_time_point_model,
time_point <= last_time_point_model,
patch_type %in% patch_type_i,
!is.na(!!sym(response_variable)))plot.patches.points(filtered_data,
patch_type_i,
response_variable) 
null_model = lmer(
get(response_variable) ~
day +
(day | culture_ID),
data = filtered_data,
REML = FALSE,
control = lmerControl(optimizer = "Nelder_Mead")
)patch_type and
patch_type : day)
full_model = lmer(
get(response_variable) ~
day +
patch_type +
patch_type : day +
(day | culture_ID),
data = filtered_data,
REML = FALSE,
control = lmerControl(optimizer = "Nelder_Mead")
)## Warning: Model failed to converge with 1 negative eigenvalue: -3.7e+01Click for model output
print(summary(full_model), digits = 1)## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
## method [lmerModLmerTest]
## Formula: get(response_variable) ~ day + patch_type + patch_type:day +
## (day | culture_ID)
## Data: filtered_data
## Control: lmerControl(optimizer = "Nelder_Mead")
##
## AIC BIC logLik deviance df.resid
## 1441.4 1461.4 -712.7 1425.4 82
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.4 -0.4 0.0 0.5 4.0
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## culture_ID (Intercept) 0e+00 0
## day 1e+02 11 NaN
## Residual 4e+05 636
## Number of obs: 90, groups: culture_ID, 15
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 3278 327 75 10.0
## day 24 18 79 1.3
## patch_typeMedium connected to medium 82 400 75 0.2
## day:patch_typeMedium connected to medium -11 22 79 -0.5
## Pr(>|t|)
## (Intercept) 2e-15 ***
## day 0.2
## patch_typeMedium connected to medium 0.8
## day:patch_typeMedium connected to medium 0.6
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) day p_Mctm
## day -0.895
## ptch_tyMctm -0.816 0.731
## dy:ptc_Mctm 0.731 -0.816 -0.895
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')Click for model ANOVA
anova(full_model)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## day 1129166 1129166 1 79.079 2.7954 0.09849 .
## patch_type 16826 16826 1 75.000 0.0417 0.83883
## day:patch_type 109948 109948 1 79.079 0.2722 0.60333
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#Create resid vs fitted (if your number of fitted values is lower than the number of rows in the filtered dataset, it's probably because you have NAs)
res_vs_fit = filtered_data %>%
mutate(predicted = fitted(full_model),
residuals = resid(full_model)) %>%
plot_ly(x = ~predicted,
y = ~residuals,
type = "scatter",
mode = "markers",
marker = list(size = 5, color = "#4C78A8"),
text = paste(" System nr: ", filtered_data$culture_ID, "<br>",
"Day: ", filtered_data$day, "<br>",
"Meta-ecosystem: ", filtered_data$patch_type, "<br>",
"Tot Biomass: ", round(filtered_data$bioarea_mm2_per_ml, digits = 2), "<br>"),
hoverinfo = "text") %>%
plotly::layout(
xaxis = list(title = "Fitted Values"),
yaxis = list(title = "Residuals")
)Click for residual plots
res_vs_fitqqnorm(resid(full_model))
model_stats_full = compute.model.stats(full_model,
null_model,
"mixed_model")
model_stats_full %>%
mutate(deltaAIC = round(deltaAIC, digits = 1),
p_value = round(p_value, digits = 3),
R2 = NULL,
evidence = "",
evidence = ifelse(p_value > 0.1,
"none",
evidence),
evidence = ifelse(p_value < 0.1,
"* weak",
evidence),
evidence = ifelse(p_value < 0.05,
"** moderate",
evidence),
evidence = ifelse(p_value < 0.01,
"*** strong",
evidence),
evidence = ifelse(p_value < 0.001,
"**** very strong",
evidence),
p_value = ifelse(p_value < 0.001,
"< 0.001",
p_value)) %>%
print()## deltaAIC p_value evidence
## 1 3.5 0.763 none
patch_type)
reduced_model = lmer(
get(response_variable) ~
day +
patch_type +
(day | culture_ID),
data = filtered_data,
REML = FALSE,
control = lmerControl(optimizer = "Nelder_Mead")
)Click for model output
print(summary(reduced_model), digits = 1)## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
## method [lmerModLmerTest]
## Formula: get(response_variable) ~ day + patch_type + (day | culture_ID)
## Data: filtered_data
## Control: lmerControl(optimizer = "Nelder_Mead")
##
## AIC BIC logLik deviance df.resid
## 1439.6 1457.1 -712.8 1425.6 83
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.4 -0.4 0.1 0.4 4.1
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## culture_ID (Intercept) 3e+03 53
## day 2e+02 14 -1.00
## Residual 4e+05 636
## Number of obs: 90, groups: culture_ID, 15
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3401 223 88 15.3 <2e-16
## day 16 10 38 1.6 0.1
## patch_typeMedium connected to medium -103 176 20 -0.6 0.6
##
## (Intercept) ***
## day
## patch_typeMedium connected to medium
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) day
## day -0.765
## ptch_tyMctm -0.526 0.000
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')#Create resid vs fitted
res_vs_fit = filtered_data %>%
mutate(predicted = fitted(reduced_model),
residuals = resid(reduced_model)) %>%
plot_ly(x = ~predicted,
y = ~residuals,
type = "scatter",
mode = "markers",
marker = list(size = 5, color = "#4C78A8"),
text = paste(" System nr: ", filtered_data$culture_ID, "<br>",
"Day: ", filtered_data$day, "<br>",
"Meta-ecosystem: ", filtered_data$patch_type, "<br>",
"Tot Biomass: ", round(filtered_data$bioarea_mm2_per_ml, digits = 2), "<br>"),
hoverinfo = "text") %>%
plotly::layout(
xaxis = list(title = "Fitted Values"),
yaxis = list(title = "Residuals")
)Click for residual plots
res_vs_fitqqnorm(resid(reduced_model))
model_stats_fixed = compute.model.stats(reduced_model,
null_model,
"mixed_model")
model_stats_fixed %>%
mutate(deltaAIC = round(deltaAIC, digits = 1),
p_value = round(p_value, digits = 3),
R2 = NULL,
evidence = "",
evidence = ifelse(p_value > 0.1,
"none",
evidence),
evidence = ifelse(p_value < 0.1,
"* weak",
evidence),
evidence = ifelse(p_value < 0.05,
"** moderate",
evidence),
evidence = ifelse(p_value < 0.01,
"*** strong",
evidence),
evidence = ifelse(p_value < 0.001,
"**** very strong",
evidence),
p_value = ifelse(p_value < 0.001,
"< 0.001",
p_value)) %>%
print()## deltaAIC p_value evidence
## 1 1.7 0.564 noneresults_table = fill.results.table(
results_table,
response_variable,
patch_type_i,
model_stats_full,
model_stats_fixed
)Populations
for(patch_type_i in patch_type_i){
print(patch_type_i)
p = ds_patches %>%
filter(patch_type == patch_type_i) %>%
group_by(day) %>%
summarise(
Ble = mean(Ble_indiv_per_ml_dominance, na.rm = TRUE),
# Ble_se = sd(Ble_indiv_per_ml_dominance, na.rm = TRUE) / sqrt(n()),
# Ble_ci = qt(c(0.025, 0.975), df = n() - 1) * Ble_se,
Cep = mean(Cep_indiv_per_ml_dominance, na.rm = TRUE),
Col = mean(Col_indiv_per_ml_dominance, na.rm = TRUE),
Eug = mean(Eug_indiv_per_ml_dominance, na.rm = TRUE),
Eup = mean(Eup_indiv_per_ml_dominance, na.rm = TRUE),
Lox = mean(Lox_indiv_per_ml_dominance, na.rm = TRUE),
Pau = mean(Pau_indiv_per_ml_dominance, na.rm = TRUE),
Pca = mean(Pca_indiv_per_ml_dominance, na.rm = TRUE),
Spi = mean(Spi_indiv_per_ml_dominance, na.rm = TRUE),
Spi_te = mean(Spi_te_indiv_per_ml_dominance, na.rm = TRUE),
Tet = mean(Tet_indiv_per_ml_dominance, na.rm = TRUE)
) %>%
pivot_longer(Ble:Tet, names_to = "species", values_to = "species_indiv_per_ml") %>%
ggplot(aes(
x = day,
y = species_indiv_per_ml,
group = interaction(day, species),
color = species
)) +
geom_point(position = position_dodge(dodging),
size = treatment_points_size) +
geom_line(
aes(group = species),
position = position_dodge(dodging),
linewidth = treatment_lines_linewidth) +
labs(x = axis_names %>%
filter(variable == "day") %>%
pull(axis_name),
y = axis_names %>%
filter(variable == "dominance") %>%
pull(axis_name)) +
coord_cartesian(ylim = c(0, 100))
print(p)
}## [1] "Medium isolated"
## [1] "Medium connected to medium"
L_S vs L_L
patch_type_i = c("Large connected to small",
"Large connected to large")Biomass
response_variable = "bioarea_mm2_per_ml"filtered_data = ds_patches %>%
filter(time_point >= first_time_point_model,
time_point <= last_time_point_model,
patch_type %in% patch_type_i,
!is.na(!!sym(response_variable)))plot.patches.points(filtered_data,
patch_type_i,
response_variable) 
null_model = lmer(
get(response_variable) ~
day +
(day | culture_ID),
data = filtered_data,
REML = FALSE,
control = lmerControl(optimizer = "Nelder_Mead")
)patch_type and
patch_type : day)
full_model = lmer(
get(response_variable) ~
day +
patch_type +
patch_type : day +
(day | culture_ID),
data = filtered_data,
REML = FALSE,
control = lmerControl(optimizer = "Nelder_Mead")
)Click for model output
print(summary(full_model), digits = 1)## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
## method [lmerModLmerTest]
## Formula: get(response_variable) ~ day + patch_type + patch_type:day +
## (day | culture_ID)
## Data: filtered_data
## Control: lmerControl(optimizer = "Nelder_Mead")
##
## AIC BIC logLik deviance df.resid
## 240.8 260.8 -112.4 224.8 82
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.23 -0.62 0.02 0.51 2.20
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## culture_ID (Intercept) 0.917 0.96
## day 0.002 0.04 -1.00
## Residual 0.627 0.79
## Number of obs: 90, groups: culture_ID, 15
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 5.475 0.418 15.044 13.1
## day -0.147 0.020 15.669 -7.2
## patch_typeLarge connected to small -0.027 0.724 15.044 0.0
## day:patch_typeLarge connected to small -0.007 0.035 15.669 -0.2
## Pr(>|t|)
## (Intercept) 1e-09 ***
## day 2e-06 ***
## patch_typeLarge connected to small 1.0
## day:patch_typeLarge connected to small 0.8
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) day p_Lcts
## day -0.965
## ptch_tyLcts -0.577 0.557
## dy:ptc_Lcts 0.557 -0.577 -0.965
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')Click for model ANOVA
anova(full_model)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## day 45.506 45.506 1 15.669 72.5264 2.856e-07 ***
## patch_type 0.001 0.001 1 15.044 0.0014 0.9712
## day:patch_type 0.023 0.023 1 15.669 0.0372 0.8495
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#Create resid vs fitted (if your number of fitted values is lower than the number of rows in the filtered dataset, it's probably because you have NAs)
res_vs_fit = filtered_data %>%
mutate(predicted = fitted(full_model),
residuals = resid(full_model)) %>%
plot_ly(x = ~predicted,
y = ~residuals,
type = "scatter",
mode = "markers",
marker = list(size = 5, color = "#4C78A8"),
text = paste(" System nr: ", filtered_data$culture_ID, "<br>",
"Day: ", filtered_data$day, "<br>",
"Meta-ecosystem: ", filtered_data$patch_type, "<br>",
"Tot Biomass: ", round(filtered_data$bioarea_mm2_per_ml, digits = 2), "<br>"),
hoverinfo = "text") %>%
plotly::layout(
xaxis = list(title = "Fitted Values"),
yaxis = list(title = "Residuals")
)Click for residual plots
res_vs_fitqqnorm(resid(full_model))
model_stats_full = compute.model.stats(full_model,
null_model,
"mixed_model")
model_stats_full %>%
mutate(deltaAIC = round(deltaAIC, digits = 1),
p_value = round(p_value, digits = 3),
R2 = NULL,
evidence = "",
evidence = ifelse(p_value > 0.1,
"none",
evidence),
evidence = ifelse(p_value < 0.1,
"* weak",
evidence),
evidence = ifelse(p_value < 0.05,
"** moderate",
evidence),
evidence = ifelse(p_value < 0.01,
"*** strong",
evidence),
evidence = ifelse(p_value < 0.001,
"**** very strong",
evidence),
p_value = ifelse(p_value < 0.001,
"< 0.001",
p_value)) %>%
print()## deltaAIC p_value evidence
## 1 3.2 0.685 none
patch_type)
reduced_model = lmer(
get(response_variable) ~
day +
patch_type +
(day | culture_ID),
data = filtered_data,
REML = FALSE,
control = lmerControl(optimizer = "Nelder_Mead")
)Click for model output
print(summary(reduced_model), digits = 1)## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
## method [lmerModLmerTest]
## Formula: get(response_variable) ~ day + patch_type + (day | culture_ID)
## Data: filtered_data
## Control: lmerControl(optimizer = "Nelder_Mead")
##
## AIC BIC logLik deviance df.resid
## 238.8 256.3 -112.4 224.8 83
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.23 -0.62 0.02 0.50 2.18
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## culture_ID (Intercept) 0.921 0.96
## day 0.002 0.04 -1.00
## Residual 0.627 0.79
## Number of obs: 90, groups: culture_ID, 15
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 5.52 0.35 16.00 15.9 3e-11
## day -0.15 0.02 15.66 -9.0 1e-07
## patch_typeLarge connected to small -0.16 0.19 40.65 -0.9 0.4
##
## (Intercept) ***
## day ***
## patch_typeLarge connected to small
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) day
## day -0.949
## ptch_tyLcts -0.182 0.000
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')#Create resid vs fitted
res_vs_fit = filtered_data %>%
mutate(predicted = fitted(reduced_model),
residuals = resid(reduced_model)) %>%
plot_ly(x = ~predicted,
y = ~residuals,
type = "scatter",
mode = "markers",
marker = list(size = 5, color = "#4C78A8"),
text = paste(" System nr: ", filtered_data$culture_ID, "<br>",
"Day: ", filtered_data$day, "<br>",
"Meta-ecosystem: ", filtered_data$patch_type, "<br>",
"Tot Biomass: ", round(filtered_data$bioarea_mm2_per_ml, digits = 2), "<br>"),
hoverinfo = "text") %>%
plotly::layout(
xaxis = list(title = "Fitted Values"),
yaxis = list(title = "Residuals")
)Click for residual plots
res_vs_fitqqnorm(resid(reduced_model))
model_stats_fixed = compute.model.stats(reduced_model,
null_model,
"mixed_model")
model_stats_fixed %>%
mutate(deltaAIC = round(deltaAIC, digits = 1),
p_value = round(p_value, digits = 3),
R2 = NULL,
evidence = "",
evidence = ifelse(p_value > 0.1,
"none",
evidence),
evidence = ifelse(p_value < 0.1,
"* weak",
evidence),
evidence = ifelse(p_value < 0.05,
"** moderate",
evidence),
evidence = ifelse(p_value < 0.01,
"*** strong",
evidence),
evidence = ifelse(p_value < 0.001,
"**** very strong",
evidence),
p_value = ifelse(p_value < 0.001,
"< 0.001",
p_value)) %>%
print()## deltaAIC p_value evidence
## 1 1.3 0.397 noneresults_table = fill.results.table(
results_table,
response_variable,
patch_type_i,
model_stats_full,
model_stats_fixed
)Shannon
response_variable = "shannon"filtered_data = ds_patches %>%
filter(time_point >= first_time_point_model,
time_point <= last_time_point_model,
patch_type %in% patch_type_i,
!is.na(!!sym(response_variable)))plot.patches.points(filtered_data,
patch_type_i,
response_variable) 
null_model = lmer(
get(response_variable) ~
day +
(day | culture_ID),
data = filtered_data,
REML = FALSE,
control = lmerControl(optimizer = "Nelder_Mead")
)patch_type and
patch_type : day)
full_model = lmer(
get(response_variable) ~
day +
patch_type +
patch_type : day +
(day | culture_ID),
data = filtered_data,
REML = FALSE,
control = lmerControl(optimizer = "Nelder_Mead")
)Click for model output
print(summary(full_model), digits = 1)## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
## method [lmerModLmerTest]
## Formula: get(response_variable) ~ day + patch_type + patch_type:day +
## (day | culture_ID)
## Data: filtered_data
## Control: lmerControl(optimizer = "Nelder_Mead")
##
## AIC BIC logLik deviance df.resid
## 23.2 43.2 -3.6 7.2 82
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.8 -0.5 0.0 0.7 1.8
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## culture_ID (Intercept) 5e-03 0.074
## day 2e-05 0.004 -1.00
## Residual 6e-02 0.250
## Number of obs: 90, groups: culture_ID, 15
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 1.500 0.094 25.418 16.0
## day -0.004 0.005 16.288 -0.8
## patch_typeLarge connected to small -0.207 0.163 25.418 -1.3
## day:patch_typeLarge connected to small 0.011 0.009 16.288 1.3
## Pr(>|t|)
## (Intercept) 9e-15 ***
## day 0.5
## patch_typeLarge connected to small 0.2
## day:patch_typeLarge connected to small 0.2
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) day p_Lcts
## day -0.939
## ptch_tyLcts -0.577 0.542
## dy:ptc_Lcts 0.542 -0.577 -0.939
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')Click for model ANOVA
anova(full_model)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## day 0.012907 0.012907 1 16.288 0.2065 0.6555
## patch_type 0.100997 0.100997 1 25.418 1.6161 0.2151
## day:patch_type 0.110436 0.110436 1 16.288 1.7671 0.2021#Create resid vs fitted (if your number of fitted values is lower than the number of rows in the filtered dataset, it's probably because you have NAs)
res_vs_fit = filtered_data %>%
mutate(predicted = fitted(full_model),
residuals = resid(full_model)) %>%
plot_ly(x = ~predicted,
y = ~residuals,
type = "scatter",
mode = "markers",
marker = list(size = 5, color = "#4C78A8"),
text = paste(" System nr: ", filtered_data$culture_ID, "<br>",
"Day: ", filtered_data$day, "<br>",
"Meta-ecosystem: ", filtered_data$patch_type, "<br>",
"Tot Biomass: ", round(filtered_data$bioarea_mm2_per_ml, digits = 2), "<br>"),
hoverinfo = "text") %>%
plotly::layout(
xaxis = list(title = "Fitted Values"),
yaxis = list(title = "Residuals")
)Click for residual plots
res_vs_fitqqnorm(resid(full_model))
model_stats_full = compute.model.stats(full_model,
null_model,
"mixed_model")
model_stats_full %>%
mutate(deltaAIC = round(deltaAIC, digits = 1),
p_value = round(p_value, digits = 3),
R2 = NULL,
evidence = "",
evidence = ifelse(p_value > 0.1,
"none",
evidence),
evidence = ifelse(p_value < 0.1,
"* weak",
evidence),
evidence = ifelse(p_value < 0.05,
"** moderate",
evidence),
evidence = ifelse(p_value < 0.01,
"*** strong",
evidence),
evidence = ifelse(p_value < 0.001,
"**** very strong",
evidence),
p_value = ifelse(p_value < 0.001,
"< 0.001",
p_value)) %>%
print()## deltaAIC p_value evidence
## 1 2.3 0.431 none
patch_type)
reduced_model = lmer(
get(response_variable) ~
day +
patch_type +
(day | culture_ID),
data = filtered_data,
REML = FALSE,
control = lmerControl(optimizer = "Nelder_Mead")
)Click for model output
print(summary(reduced_model), digits = 1)## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
## method [lmerModLmerTest]
## Formula: get(response_variable) ~ day + patch_type + (day | culture_ID)
## Data: filtered_data
## Control: lmerControl(optimizer = "Nelder_Mead")
##
## AIC BIC logLik deviance df.resid
## 22.9 40.4 -4.5 8.9 83
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.8 -0.5 0.1 0.7 1.9
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## culture_ID (Intercept) 1e-02 0.114
## day 5e-05 0.007 -1.00
## Residual 6e-02 0.250
## Number of obs: 90, groups: culture_ID, 15
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1e+00 8e-02 2e+01 17.4 7e-15
## day 5e-05 4e-03 2e+01 0.0 1.0
## patch_typeLarge connected to small -5e-03 6e-02 7e+01 -0.1 0.9
##
## (Intercept) ***
## day
## patch_typeLarge connected to small
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) day
## day -0.919
## ptch_tyLcts -0.228 0.000
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')#Create resid vs fitted
res_vs_fit = filtered_data %>%
mutate(predicted = fitted(reduced_model),
residuals = resid(reduced_model)) %>%
plot_ly(x = ~predicted,
y = ~residuals,
type = "scatter",
mode = "markers",
marker = list(size = 5, color = "#4C78A8"),
text = paste(" System nr: ", filtered_data$culture_ID, "<br>",
"Day: ", filtered_data$day, "<br>",
"Meta-ecosystem: ", filtered_data$patch_type, "<br>",
"Tot Biomass: ", round(filtered_data$bioarea_mm2_per_ml, digits = 2), "<br>"),
hoverinfo = "text") %>%
plotly::layout(
xaxis = list(title = "Fitted Values"),
yaxis = list(title = "Residuals")
)Click for residual plots
res_vs_fitqqnorm(resid(reduced_model))
model_stats_fixed = compute.model.stats(reduced_model,
null_model,
"mixed_model")
model_stats_fixed %>%
mutate(deltaAIC = round(deltaAIC, digits = 1),
p_value = round(p_value, digits = 3),
R2 = NULL,
evidence = "",
evidence = ifelse(p_value > 0.1,
"none",
evidence),
evidence = ifelse(p_value < 0.1,
"* weak",
evidence),
evidence = ifelse(p_value < 0.05,
"** moderate",
evidence),
evidence = ifelse(p_value < 0.01,
"*** strong",
evidence),
evidence = ifelse(p_value < 0.001,
"**** very strong",
evidence),
p_value = ifelse(p_value < 0.001,
"< 0.001",
p_value)) %>%
print()## deltaAIC p_value evidence
## 1 2 0.932 noneresults_table = fill.results.table(
results_table,
response_variable,
patch_type_i,
model_stats_full,
model_stats_fixed
)Richness
response_variable = "species_richness"filtered_data = ds_patches %>%
filter(time_point >= first_time_point_model,
time_point <= last_time_point_model,
patch_type %in% patch_type_i,
!is.na(!!sym(response_variable)))plot.patches.points(filtered_data,
patch_type_i,
response_variable) 
null_model = lmer(
get(response_variable) ~
day +
(day | culture_ID),
data = filtered_data,
REML = FALSE,
control = lmerControl(optimizer = "Nelder_Mead")
)patch_type and
patch_type : day)
full_model = lmer(
get(response_variable) ~
day +
patch_type +
patch_type : day +
(day | culture_ID),
data = filtered_data,
REML = FALSE,
control = lmerControl(optimizer = "Nelder_Mead")
)Click for model output
print(summary(full_model), digits = 1)## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
## method [lmerModLmerTest]
## Formula: get(response_variable) ~ day + patch_type + patch_type:day +
## (day | culture_ID)
## Data: filtered_data
## Control: lmerControl(optimizer = "Nelder_Mead")
##
## AIC BIC logLik deviance df.resid
## 319.0 339.0 -151.5 303.0 82
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.6 -0.5 0.1 0.5 2.0
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## culture_ID (Intercept) 0.127 0.36
## day 0.001 0.04 -1.00
## Residual 1.574 1.25
## Number of obs: 90, groups: culture_ID, 15
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 7.22 0.47 48.53 15.3
## day -0.05 0.03 25.62 -1.8
## patch_typeLarge connected to small -0.70 0.81 48.53 -0.9
## day:patch_typeLarge connected to small 0.04 0.05 25.62 1.0
## Pr(>|t|)
## (Intercept) <2e-16 ***
## day 0.09 .
## patch_typeLarge connected to small 0.40
## day:patch_typeLarge connected to small 0.34
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) day p_Lcts
## day -0.921
## ptch_tyLcts -0.577 0.532
## dy:ptc_Lcts 0.532 -0.577 -0.921
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')Click for model ANOVA
anova(full_model)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## day 1.8895 1.8895 1 25.619 1.2004 0.2834
## patch_type 1.1533 1.1533 1 48.528 0.7327 0.3962
## day:patch_type 1.4823 1.4823 1 25.619 0.9417 0.3409#Create resid vs fitted (if your number of fitted values is lower than the number of rows in the filtered dataset, it's probably because you have NAs)
res_vs_fit = filtered_data %>%
mutate(predicted = fitted(full_model),
residuals = resid(full_model)) %>%
plot_ly(x = ~predicted,
y = ~residuals,
type = "scatter",
mode = "markers",
marker = list(size = 5, color = "#4C78A8"),
text = paste(" System nr: ", filtered_data$culture_ID, "<br>",
"Day: ", filtered_data$day, "<br>",
"Meta-ecosystem: ", filtered_data$patch_type, "<br>",
"Tot Biomass: ", round(filtered_data$bioarea_mm2_per_ml, digits = 2), "<br>"),
hoverinfo = "text") %>%
plotly::layout(
xaxis = list(title = "Fitted Values"),
yaxis = list(title = "Residuals")
)Click for residual plots
res_vs_fitqqnorm(resid(full_model))
model_stats_full = compute.model.stats(full_model,
null_model,
"mixed_model")
model_stats_full %>%
mutate(deltaAIC = round(deltaAIC, digits = 1),
p_value = round(p_value, digits = 3),
R2 = NULL,
evidence = "",
evidence = ifelse(p_value > 0.1,
"none",
evidence),
evidence = ifelse(p_value < 0.1,
"* weak",
evidence),
evidence = ifelse(p_value < 0.05,
"** moderate",
evidence),
evidence = ifelse(p_value < 0.01,
"*** strong",
evidence),
evidence = ifelse(p_value < 0.001,
"**** very strong",
evidence),
p_value = ifelse(p_value < 0.001,
"< 0.001",
p_value)) %>%
print()## deltaAIC p_value evidence
## 1 3.1 0.628 none
patch_type)
reduced_model = lmer(
get(response_variable) ~
day +
patch_type +
(day | culture_ID),
data = filtered_data,
REML = FALSE,
control = lmerControl(optimizer = "Nelder_Mead")
)Click for model output
print(summary(reduced_model), digits = 1)## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
## method [lmerModLmerTest]
## Formula: get(response_variable) ~ day + patch_type + (day | culture_ID)
## Data: filtered_data
## Control: lmerControl(optimizer = "Nelder_Mead")
##
## AIC BIC logLik deviance df.resid
## 317.9 335.4 -152.0 303.9 83
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.6 -0.5 0.2 0.5 2.0
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## culture_ID (Intercept) 0.168 0.41
## day 0.002 0.04 -1.00
## Residual 1.583 1.26
## Number of obs: 90, groups: culture_ID, 15
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 6.97 0.40 50.73 17.3 <2e-16
## day -0.03 0.02 23.41 -1.5 0.2
## patch_typeLarge connected to small 0.03 0.32 28.02 0.1 0.9
##
## (Intercept) ***
## day
## patch_typeLarge connected to small
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) day
## day -0.891
## ptch_tyLcts -0.262 0.000
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')#Create resid vs fitted
res_vs_fit = filtered_data %>%
mutate(predicted = fitted(reduced_model),
residuals = resid(reduced_model)) %>%
plot_ly(x = ~predicted,
y = ~residuals,
type = "scatter",
mode = "markers",
marker = list(size = 5, color = "#4C78A8"),
text = paste(" System nr: ", filtered_data$culture_ID, "<br>",
"Day: ", filtered_data$day, "<br>",
"Meta-ecosystem: ", filtered_data$patch_type, "<br>",
"Tot Biomass: ", round(filtered_data$bioarea_mm2_per_ml, digits = 2), "<br>"),
hoverinfo = "text") %>%
plotly::layout(
xaxis = list(title = "Fitted Values"),
yaxis = list(title = "Residuals")
)Click for residual plots
res_vs_fitqqnorm(resid(reduced_model))
model_stats_fixed = compute.model.stats(reduced_model,
null_model,
"mixed_model")
model_stats_fixed %>%
mutate(deltaAIC = round(deltaAIC, digits = 1),
p_value = round(p_value, digits = 3),
R2 = NULL,
evidence = "",
evidence = ifelse(p_value > 0.1,
"none",
evidence),
evidence = ifelse(p_value < 0.1,
"* weak",
evidence),
evidence = ifelse(p_value < 0.05,
"** moderate",
evidence),
evidence = ifelse(p_value < 0.01,
"*** strong",
evidence),
evidence = ifelse(p_value < 0.001,
"**** very strong",
evidence),
p_value = ifelse(p_value < 0.001,
"< 0.001",
p_value)) %>%
print()## deltaAIC p_value evidence
## 1 2 0.933 noneresults_table = fill.results.table(
results_table,
response_variable,
patch_type_i,
model_stats_full,
model_stats_fixed
)Evenness
response_variable = "evenness_pielou"filtered_data = ds_patches %>%
filter(time_point >= first_time_point_model,
time_point <= last_time_point_model,
patch_type %in% patch_type_i,
!is.na(!!sym(response_variable)))plot.patches.points(filtered_data,
patch_type_i,
response_variable) 
null_model = lmer(
get(response_variable) ~
day +
(day | culture_ID),
data = filtered_data,
REML = FALSE,
control = lmerControl(optimizer = "Nelder_Mead")
)patch_type and
patch_type : day)
full_model = lmer(
get(response_variable) ~
day +
patch_type +
patch_type : day +
(day | culture_ID),
data = filtered_data,
REML = FALSE,
control = lmerControl(optimizer = "Nelder_Mead")
)Click for model output
print(summary(full_model), digits = 1)## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
## method [lmerModLmerTest]
## Formula: get(response_variable) ~ day + patch_type + patch_type:day +
## (day | culture_ID)
## Data: filtered_data
## Control: lmerControl(optimizer = "Nelder_Mead")
##
## AIC BIC logLik deviance df.resid
## -148.7 -128.7 82.4 -164.7 82
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.1 -0.5 0.2 0.6 2.0
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## culture_ID (Intercept) 6e-03 0.079
## day 2e-05 0.004 -1.00
## Residual 9e-03 0.093
## Number of obs: 90, groups: culture_ID, 15
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 0.755 0.042 15.114 17.9
## day 0.002 0.002 15.145 0.7
## patch_typeLarge connected to small -0.044 0.073 15.114 -0.6
## day:patch_typeLarge connected to small 0.002 0.004 15.145 0.7
## Pr(>|t|)
## (Intercept) 1e-11 ***
## day 0.5
## patch_typeLarge connected to small 0.6
## day:patch_typeLarge connected to small 0.5
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) day p_Lcts
## day -0.958
## ptch_tyLcts -0.577 0.553
## dy:ptc_Lcts 0.553 -0.577 -0.958
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')Click for model ANOVA
anova(full_model)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## day 0.0189475 0.0189475 1 15.145 2.1734 0.1609
## patch_type 0.0032234 0.0032234 1 15.114 0.3698 0.5522
## day:patch_type 0.0037138 0.0037138 1 15.145 0.4260 0.5237#Create resid vs fitted (if your number of fitted values is lower than the number of rows in the filtered dataset, it's probably because you have NAs)
res_vs_fit = filtered_data %>%
mutate(predicted = fitted(full_model),
residuals = resid(full_model)) %>%
plot_ly(x = ~predicted,
y = ~residuals,
type = "scatter",
mode = "markers",
marker = list(size = 5, color = "#4C78A8"),
text = paste(" System nr: ", filtered_data$culture_ID, "<br>",
"Day: ", filtered_data$day, "<br>",
"Meta-ecosystem: ", filtered_data$patch_type, "<br>",
"Tot Biomass: ", round(filtered_data$bioarea_mm2_per_ml, digits = 2), "<br>"),
hoverinfo = "text") %>%
plotly::layout(
xaxis = list(title = "Fitted Values"),
yaxis = list(title = "Residuals")
)Click for residual plots
res_vs_fitqqnorm(resid(full_model))
model_stats_full = compute.model.stats(full_model,
null_model,
"mixed_model")
model_stats_full %>%
mutate(deltaAIC = round(deltaAIC, digits = 1),
p_value = round(p_value, digits = 3),
R2 = NULL,
evidence = "",
evidence = ifelse(p_value > 0.1,
"none",
evidence),
evidence = ifelse(p_value < 0.1,
"* weak",
evidence),
evidence = ifelse(p_value < 0.05,
"** moderate",
evidence),
evidence = ifelse(p_value < 0.01,
"*** strong",
evidence),
evidence = ifelse(p_value < 0.001,
"**** very strong",
evidence),
p_value = ifelse(p_value < 0.001,
"< 0.001",
p_value)) %>%
print()## deltaAIC p_value evidence
## 1 3.6 0.809 none
patch_type)
reduced_model = lmer(
get(response_variable) ~
day +
patch_type +
(day | culture_ID),
data = filtered_data,
REML = FALSE,
control = lmerControl(optimizer = "Nelder_Mead")
)Click for model output
print(summary(reduced_model), digits = 1)## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
## method [lmerModLmerTest]
## Formula: get(response_variable) ~ day + patch_type + (day | culture_ID)
## Data: filtered_data
## Control: lmerControl(optimizer = "Nelder_Mead")
##
## AIC BIC logLik deviance df.resid
## -150.3 -132.8 82.2 -164.3 83
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.1 -0.4 0.2 0.6 2.1
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## culture_ID (Intercept) 7e-03 0.082
## day 2e-05 0.004 -1.00
## Residual 9e-03 0.093
## Number of obs: 90, groups: culture_ID, 15
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 7e-01 4e-02 2e+01 20.8 4e-13
## day 2e-03 2e-03 2e+01 1.3 0.2
## patch_typeLarge connected to small 1e-03 2e-02 7e+01 0.1 1.0
##
## (Intercept) ***
## day
## patch_typeLarge connected to small
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) day
## day -0.940
## ptch_tyLcts -0.197 0.000
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')#Create resid vs fitted
res_vs_fit = filtered_data %>%
mutate(predicted = fitted(reduced_model),
residuals = resid(reduced_model)) %>%
plot_ly(x = ~predicted,
y = ~residuals,
type = "scatter",
mode = "markers",
marker = list(size = 5, color = "#4C78A8"),
text = paste(" System nr: ", filtered_data$culture_ID, "<br>",
"Day: ", filtered_data$day, "<br>",
"Meta-ecosystem: ", filtered_data$patch_type, "<br>",
"Tot Biomass: ", round(filtered_data$bioarea_mm2_per_ml, digits = 2), "<br>"),
hoverinfo = "text") %>%
plotly::layout(
xaxis = list(title = "Fitted Values"),
yaxis = list(title = "Residuals")
)Click for residual plots
res_vs_fitqqnorm(resid(reduced_model))
model_stats_fixed = compute.model.stats(reduced_model,
null_model,
"mixed_model")
model_stats_fixed %>%
mutate(deltaAIC = round(deltaAIC, digits = 1),
p_value = round(p_value, digits = 3),
R2 = NULL,
evidence = "",
evidence = ifelse(p_value > 0.1,
"none",
evidence),
evidence = ifelse(p_value < 0.1,
"* weak",
evidence),
evidence = ifelse(p_value < 0.05,
"** moderate",
evidence),
evidence = ifelse(p_value < 0.01,
"*** strong",
evidence),
evidence = ifelse(p_value < 0.001,
"**** very strong",
evidence),
p_value = ifelse(p_value < 0.001,
"< 0.001",
p_value)) %>%
print()## deltaAIC p_value evidence
## 1 2 0.953 noneresults_table = fill.results.table(
results_table,
response_variable,
patch_type_i,
model_stats_full,
model_stats_fixed
)Median Size
response_variable = "median_body_area_µm2"filtered_data = ds_patches %>%
filter(time_point >= first_time_point_model,
time_point <= last_time_point_model,
patch_type %in% patch_type_i,
!is.na(!!sym(response_variable)))plot.patches.points(filtered_data,
patch_type_i,
response_variable) 
null_model = lmer(
get(response_variable) ~
day +
(day | culture_ID),
data = filtered_data,
REML = FALSE,
control = lmerControl(optimizer = "Nelder_Mead")
)patch_type and
patch_type : day)
full_model = lmer(
get(response_variable) ~
day +
patch_type +
patch_type : day +
(day | culture_ID),
data = filtered_data,
REML = FALSE,
control = lmerControl(optimizer = "Nelder_Mead")
)Click for model output
print(summary(full_model), digits = 1)## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
## method [lmerModLmerTest]
## Formula: get(response_variable) ~ day + patch_type + patch_type:day +
## (day | culture_ID)
## Data: filtered_data
## Control: lmerControl(optimizer = "Nelder_Mead")
##
## AIC BIC logLik deviance df.resid
## 1399.6 1419.6 -691.8 1383.6 82
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.82 -0.67 -0.03 0.49 2.74
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## culture_ID (Intercept) 8e+04 278
## day 5e+02 23 -1.00
## Residual 2e+05 498
## Number of obs: 90, groups: culture_ID, 15
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 2389 202 30 11.9
## day 72 12 19 6.1
## patch_typeLarge connected to small 47 349 30 0.1
## day:patch_typeLarge connected to small -6 20 19 -0.3
## Pr(>|t|)
## (Intercept) 8e-13 ***
## day 8e-06 ***
## patch_typeLarge connected to small 0.9
## day:patch_typeLarge connected to small 0.8
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) day p_Lcts
## day -0.934
## ptch_tyLcts -0.577 0.539
## dy:ptc_Lcts 0.539 -0.577 -0.934
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')Click for model ANOVA
anova(full_model)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## day 11210561 11210561 1 19.055 45.1503 1.986e-06 ***
## patch_type 4563 4563 1 29.831 0.0184 0.8931
## day:patch_type 18765 18765 1 19.055 0.0756 0.7863
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#Create resid vs fitted (if your number of fitted values is lower than the number of rows in the filtered dataset, it's probably because you have NAs)
res_vs_fit = filtered_data %>%
mutate(predicted = fitted(full_model),
residuals = resid(full_model)) %>%
plot_ly(x = ~predicted,
y = ~residuals,
type = "scatter",
mode = "markers",
marker = list(size = 5, color = "#4C78A8"),
text = paste(" System nr: ", filtered_data$culture_ID, "<br>",
"Day: ", filtered_data$day, "<br>",
"Meta-ecosystem: ", filtered_data$patch_type, "<br>",
"Tot Biomass: ", round(filtered_data$bioarea_mm2_per_ml, digits = 2), "<br>"),
hoverinfo = "text") %>%
plotly::layout(
xaxis = list(title = "Fitted Values"),
yaxis = list(title = "Residuals")
)Click for residual plots
res_vs_fitqqnorm(resid(full_model))
model_stats_full = compute.model.stats(full_model,
null_model,
"mixed_model")
model_stats_full %>%
mutate(deltaAIC = round(deltaAIC, digits = 1),
p_value = round(p_value, digits = 3),
R2 = NULL,
evidence = "",
evidence = ifelse(p_value > 0.1,
"none",
evidence),
evidence = ifelse(p_value < 0.1,
"* weak",
evidence),
evidence = ifelse(p_value < 0.05,
"** moderate",
evidence),
evidence = ifelse(p_value < 0.01,
"*** strong",
evidence),
evidence = ifelse(p_value < 0.001,
"**** very strong",
evidence),
p_value = ifelse(p_value < 0.001,
"< 0.001",
p_value)) %>%
print()## deltaAIC p_value evidence
## 1 3.8 0.91 none
patch_type)
reduced_model = lmer(
get(response_variable) ~
day +
patch_type +
(day | culture_ID),
data = filtered_data,
REML = FALSE,
control = lmerControl(optimizer = "Nelder_Mead")
)Click for model output
print(summary(reduced_model), digits = 1)## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
## method [lmerModLmerTest]
## Formula: get(response_variable) ~ day + patch_type + (day | culture_ID)
## Data: filtered_data
## Control: lmerControl(optimizer = "Nelder_Mead")
##
## AIC BIC logLik deviance df.resid
## 1397.7 1415.2 -691.9 1383.7 83
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.79 -0.66 -0.04 0.49 2.76
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## culture_ID (Intercept) 8e+04 280
## day 5e+02 23 -1.00
## Residual 2e+05 498
## Number of obs: 90, groups: culture_ID, 15
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2419 170 34 14.2 7e-16
## day 70 10 19 7.2 8e-07
## patch_typeLarge connected to small -42 125 37 -0.3 0.7
##
## (Intercept) ***
## day ***
## patch_typeLarge connected to small
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) day
## day -0.906
## ptch_tyLcts -0.245 0.000
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')#Create resid vs fitted
res_vs_fit = filtered_data %>%
mutate(predicted = fitted(reduced_model),
residuals = resid(reduced_model)) %>%
plot_ly(x = ~predicted,
y = ~residuals,
type = "scatter",
mode = "markers",
marker = list(size = 5, color = "#4C78A8"),
text = paste(" System nr: ", filtered_data$culture_ID, "<br>",
"Day: ", filtered_data$day, "<br>",
"Meta-ecosystem: ", filtered_data$patch_type, "<br>",
"Tot Biomass: ", round(filtered_data$bioarea_mm2_per_ml, digits = 2), "<br>"),
hoverinfo = "text") %>%
plotly::layout(
xaxis = list(title = "Fitted Values"),
yaxis = list(title = "Residuals")
)Click for residual plots
res_vs_fitqqnorm(resid(reduced_model))
model_stats_fixed = compute.model.stats(reduced_model,
null_model,
"mixed_model")
model_stats_fixed %>%
mutate(deltaAIC = round(deltaAIC, digits = 1),
p_value = round(p_value, digits = 3),
R2 = NULL,
evidence = "",
evidence = ifelse(p_value > 0.1,
"none",
evidence),
evidence = ifelse(p_value < 0.1,
"* weak",
evidence),
evidence = ifelse(p_value < 0.05,
"** moderate",
evidence),
evidence = ifelse(p_value < 0.01,
"*** strong",
evidence),
evidence = ifelse(p_value < 0.001,
"**** very strong",
evidence),
p_value = ifelse(p_value < 0.001,
"< 0.001",
p_value)) %>%
print()## deltaAIC p_value evidence
## 1 1.9 0.735 noneresults_table = fill.results.table(
results_table,
response_variable,
patch_type_i,
model_stats_full,
model_stats_fixed
)Populations
for(patch_type_i in patch_type_i){
print(patch_type_i)
p = ds_patches %>%
filter(patch_type == patch_type_i) %>%
group_by(day) %>%
summarise(
Ble = mean(Ble_indiv_per_ml_dominance, na.rm = TRUE),
# Ble_se = sd(Ble_indiv_per_ml_dominance, na.rm = TRUE) / sqrt(n()),
# Ble_ci = qt(c(0.025, 0.975), df = n() - 1) * Ble_se,
Cep = mean(Cep_indiv_per_ml_dominance, na.rm = TRUE),
Col = mean(Col_indiv_per_ml_dominance, na.rm = TRUE),
Eug = mean(Eug_indiv_per_ml_dominance, na.rm = TRUE),
Eup = mean(Eup_indiv_per_ml_dominance, na.rm = TRUE),
Lox = mean(Lox_indiv_per_ml_dominance, na.rm = TRUE),
Pau = mean(Pau_indiv_per_ml_dominance, na.rm = TRUE),
Pca = mean(Pca_indiv_per_ml_dominance, na.rm = TRUE),
Spi = mean(Spi_indiv_per_ml_dominance, na.rm = TRUE),
Spi_te = mean(Spi_te_indiv_per_ml_dominance, na.rm = TRUE),
Tet = mean(Tet_indiv_per_ml_dominance, na.rm = TRUE)
) %>%
pivot_longer(Ble:Tet, names_to = "species", values_to = "species_indiv_per_ml") %>%
ggplot(aes(
x = day,
y = species_indiv_per_ml,
group = interaction(day, species),
color = species
)) +
geom_point(position = position_dodge(dodging),
size = treatment_points_size) +
geom_line(
aes(group = species),
position = position_dodge(dodging),
linewidth = treatment_lines_linewidth) +
labs(x = axis_names %>%
filter(variable == "day") %>%
pull(axis_name),
y = axis_names %>%
filter(variable == "dominance") %>%
pull(axis_name)) +
coord_cartesian(ylim = c(0, 100))
print(p)
}## [1] "Large connected to small"
## [1] "Large connected to large"
L_L vs L
patch_type_i = c("Large connected to large",
"Large isolated")Biomass
response_variable = "bioarea_mm2_per_ml"filtered_data = ds_patches %>%
filter(time_point >= first_time_point_model,
time_point <= last_time_point_model,
patch_type %in% patch_type_i,
!is.na(!!sym(response_variable)))plot.patches.points(filtered_data,
patch_type_i,
response_variable) 
null_model = lmer(
get(response_variable) ~
day +
(day | culture_ID),
data = filtered_data,
REML = FALSE,
control = lmerControl(optimizer = "Nelder_Mead")
)patch_type and
patch_type : day)
full_model = lmer(
get(response_variable) ~
day +
patch_type +
patch_type : day +
(day | culture_ID),
data = filtered_data,
REML = FALSE,
control = lmerControl(optimizer = "Nelder_Mead")
)Click for model output
print(summary(full_model), digits = 1)## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
## method [lmerModLmerTest]
## Formula: get(response_variable) ~ day + patch_type + patch_type:day +
## (day | culture_ID)
## Data: filtered_data
## Control: lmerControl(optimizer = "Nelder_Mead")
##
## AIC BIC logLik deviance df.resid
## 247.1 267.1 -115.6 231.1 82
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.83 -0.64 0.01 0.51 2.37
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## culture_ID (Intercept) 0.325 0.57
## day 0.001 0.03 -0.83
## Residual 0.635 0.80
## Number of obs: 90, groups: culture_ID, 15
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 4.95 0.48 15.00 10.2
## day -0.09 0.03 15.00 -3.4
## patch_typeLarge connected to large 0.53 0.59 15.00 0.9
## day:patch_typeLarge connected to large -0.06 0.03 15.00 -1.8
## Pr(>|t|)
## (Intercept) 4e-08 ***
## day 0.004 **
## patch_typeLarge connected to large 0.385
## day:patch_typeLarge connected to large 0.089 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) day p_Lctl
## day -0.903
## ptch_tyLctl -0.816 0.737
## dy:ptc_Lctl 0.737 -0.816 -0.903Click for model ANOVA
anova(full_model)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## day 34.918 34.918 1 15 55.0124 2.159e-06 ***
## patch_type 0.508 0.508 1 15 0.8008 0.38499
## day:patch_type 2.100 2.100 1 15 3.3084 0.08894 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#Create resid vs fitted (if your number of fitted values is lower than the number of rows in the filtered dataset, it's probably because you have NAs)
res_vs_fit = filtered_data %>%
mutate(predicted = fitted(full_model),
residuals = resid(full_model)) %>%
plot_ly(x = ~predicted,
y = ~residuals,
type = "scatter",
mode = "markers",
marker = list(size = 5, color = "#4C78A8"),
text = paste(" System nr: ", filtered_data$culture_ID, "<br>",
"Day: ", filtered_data$day, "<br>",
"Meta-ecosystem: ", filtered_data$patch_type, "<br>",
"Tot Biomass: ", round(filtered_data$bioarea_mm2_per_ml, digits = 2), "<br>"),
hoverinfo = "text") %>%
plotly::layout(
xaxis = list(title = "Fitted Values"),
yaxis = list(title = "Residuals")
)Click for residual plots
res_vs_fitqqnorm(resid(full_model))
model_stats_full = compute.model.stats(full_model,
null_model,
"mixed_model")
model_stats_full %>%
mutate(deltaAIC = round(deltaAIC, digits = 1),
p_value = round(p_value, digits = 3),
R2 = NULL,
evidence = "",
evidence = ifelse(p_value > 0.1,
"none",
evidence),
evidence = ifelse(p_value < 0.1,
"* weak",
evidence),
evidence = ifelse(p_value < 0.05,
"** moderate",
evidence),
evidence = ifelse(p_value < 0.01,
"*** strong",
evidence),
evidence = ifelse(p_value < 0.001,
"**** very strong",
evidence),
p_value = ifelse(p_value < 0.001,
"< 0.001",
p_value)) %>%
print()## deltaAIC p_value evidence
## 1 -1.3 0.071 * weak
patch_type)
reduced_model = lmer(
get(response_variable) ~
day +
patch_type +
(day | culture_ID),
data = filtered_data,
REML = FALSE,
control = lmerControl(optimizer = "Nelder_Mead")
)Click for model output
print(summary(reduced_model), digits = 1)## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
## method [lmerModLmerTest]
## Formula: get(response_variable) ~ day + patch_type + (day | culture_ID)
## Data: filtered_data
## Control: lmerControl(optimizer = "Nelder_Mead")
##
## AIC BIC logLik deviance df.resid
## 248.1 265.6 -117.1 234.1 83
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.02 -0.58 -0.02 0.48 2.41
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## culture_ID (Intercept) 0.535 0.73
## day 0.002 0.04 -0.90
## Residual 0.635 0.80
## Number of obs: 90, groups: culture_ID, 15
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 5.59 0.35 20.62 16 4e-13
## day -0.13 0.02 15.00 -8 1e-06
## patch_typeLarge connected to large -0.44 0.25 15.00 -2 0.1
##
## (Intercept) ***
## day ***
## patch_typeLarge connected to large
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) day
## day -0.801
## ptch_tyLctl -0.488 0.000#Create resid vs fitted
res_vs_fit = filtered_data %>%
mutate(predicted = fitted(reduced_model),
residuals = resid(reduced_model)) %>%
plot_ly(x = ~predicted,
y = ~residuals,
type = "scatter",
mode = "markers",
marker = list(size = 5, color = "#4C78A8"),
text = paste(" System nr: ", filtered_data$culture_ID, "<br>",
"Day: ", filtered_data$day, "<br>",
"Meta-ecosystem: ", filtered_data$patch_type, "<br>",
"Tot Biomass: ", round(filtered_data$bioarea_mm2_per_ml, digits = 2), "<br>"),
hoverinfo = "text") %>%
plotly::layout(
xaxis = list(title = "Fitted Values"),
yaxis = list(title = "Residuals")
)Click for residual plots
res_vs_fitqqnorm(resid(reduced_model))
model_stats_fixed = compute.model.stats(reduced_model,
null_model,
"mixed_model")
model_stats_fixed %>%
mutate(deltaAIC = round(deltaAIC, digits = 1),
p_value = round(p_value, digits = 3),
R2 = NULL,
evidence = "",
evidence = ifelse(p_value > 0.1,
"none",
evidence),
evidence = ifelse(p_value < 0.1,
"* weak",
evidence),
evidence = ifelse(p_value < 0.05,
"** moderate",
evidence),
evidence = ifelse(p_value < 0.01,
"*** strong",
evidence),
evidence = ifelse(p_value < 0.001,
"**** very strong",
evidence),
p_value = ifelse(p_value < 0.001,
"< 0.001",
p_value)) %>%
print()## deltaAIC p_value evidence
## 1 -0.3 0.13 noneresults_table = fill.results.table(
results_table,
response_variable,
patch_type_i,
model_stats_full,
model_stats_fixed
)Shannon
response_variable = "shannon"filtered_data = ds_patches %>%
filter(time_point >= first_time_point_model,
time_point <= last_time_point_model,
patch_type %in% patch_type_i,
!is.na(!!sym(response_variable)))plot.patches.points(filtered_data,
patch_type_i,
response_variable) 
null_model = lmer(
get(response_variable) ~
day +
(day | culture_ID),
data = filtered_data,
REML = FALSE,
control = lmerControl(optimizer = "Nelder_Mead")
)patch_type and
patch_type : day)
full_model = lmer(
get(response_variable) ~
day +
patch_type +
patch_type : day +
(day | culture_ID),
data = filtered_data,
REML = FALSE,
control = lmerControl(optimizer = "Nelder_Mead")
)Click for model output
print(summary(full_model), digits = 1)## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
## method [lmerModLmerTest]
## Formula: get(response_variable) ~ day + patch_type + patch_type:day +
## (day | culture_ID)
## Data: filtered_data
## Control: lmerControl(optimizer = "Nelder_Mead")
##
## AIC BIC logLik deviance df.resid
## 5.5 25.5 5.3 -10.5 82
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.76 -0.60 -0.06 0.78 1.59
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## culture_ID (Intercept) 2e-02 0.148
## day 8e-05 0.009 -1.00
## Residual 5e-02 0.221
## Number of obs: 90, groups: culture_ID, 15
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 1.831 0.132 16.742 14
## day -0.017 0.007 15.185 -2
## patch_typeLarge connected to large -0.332 0.161 16.742 -2
## day:patch_typeLarge connected to large 0.013 0.009 15.185 2
## Pr(>|t|)
## (Intercept) 1e-10 ***
## day 0.03 *
## patch_typeLarge connected to large 0.06 .
## day:patch_typeLarge connected to large 0.15
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) day p_Lctl
## day -0.951
## ptch_tyLctl -0.816 0.777
## dy:ptc_Lctl 0.777 -0.816 -0.951
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')Click for model ANOVA
anova(full_model)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## day 0.27607 0.27607 1 15.184 5.6629 0.03085 *
## patch_type 0.20643 0.20643 1 16.742 4.2345 0.05553 .
## day:patch_type 0.11339 0.11339 1 15.184 2.3259 0.14780
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#Create resid vs fitted (if your number of fitted values is lower than the number of rows in the filtered dataset, it's probably because you have NAs)
res_vs_fit = filtered_data %>%
mutate(predicted = fitted(full_model),
residuals = resid(full_model)) %>%
plot_ly(x = ~predicted,
y = ~residuals,
type = "scatter",
mode = "markers",
marker = list(size = 5, color = "#4C78A8"),
text = paste(" System nr: ", filtered_data$culture_ID, "<br>",
"Day: ", filtered_data$day, "<br>",
"Meta-ecosystem: ", filtered_data$patch_type, "<br>",
"Tot Biomass: ", round(filtered_data$bioarea_mm2_per_ml, digits = 2), "<br>"),
hoverinfo = "text") %>%
plotly::layout(
xaxis = list(title = "Fitted Values"),
yaxis = list(title = "Residuals")
)Click for residual plots
res_vs_fitqqnorm(resid(full_model))
model_stats_full = compute.model.stats(full_model,
null_model,
"mixed_model")
model_stats_full %>%
mutate(deltaAIC = round(deltaAIC, digits = 1),
p_value = round(p_value, digits = 3),
R2 = NULL,
evidence = "",
evidence = ifelse(p_value > 0.1,
"none",
evidence),
evidence = ifelse(p_value < 0.1,
"* weak",
evidence),
evidence = ifelse(p_value < 0.05,
"** moderate",
evidence),
evidence = ifelse(p_value < 0.01,
"*** strong",
evidence),
evidence = ifelse(p_value < 0.001,
"**** very strong",
evidence),
p_value = ifelse(p_value < 0.001,
"< 0.001",
p_value)) %>%
print()## deltaAIC p_value evidence
## 1 -1.5 0.064 * weak
patch_type)
reduced_model = lmer(
get(response_variable) ~
day +
patch_type +
(day | culture_ID),
data = filtered_data,
REML = FALSE,
control = lmerControl(optimizer = "Nelder_Mead")
)Click for model output
print(summary(reduced_model), digits = 1)## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
## method [lmerModLmerTest]
## Formula: get(response_variable) ~ day + patch_type + (day | culture_ID)
## Data: filtered_data
## Control: lmerControl(optimizer = "Nelder_Mead")
##
## AIC BIC logLik deviance df.resid
## 5.6 23.1 4.2 -8.4 83
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.73 -0.67 -0.01 0.77 1.68
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## culture_ID (Intercept) 3e-02 0.18
## day 1e-04 0.01 -1.00
## Residual 5e-02 0.22
## Number of obs: 90, groups: culture_ID, 15
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.675 0.087 21.683 19 5e-15
## day -0.008 0.004 15.074 -2 0.08
## patch_typeLarge connected to large -0.098 0.050 66.017 -2 0.05
##
## (Intercept) ***
## day .
## patch_typeLarge connected to large .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) day
## day -0.886
## ptch_tyLctl -0.379 0.000
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')#Create resid vs fitted
res_vs_fit = filtered_data %>%
mutate(predicted = fitted(reduced_model),
residuals = resid(reduced_model)) %>%
plot_ly(x = ~predicted,
y = ~residuals,
type = "scatter",
mode = "markers",
marker = list(size = 5, color = "#4C78A8"),
text = paste(" System nr: ", filtered_data$culture_ID, "<br>",
"Day: ", filtered_data$day, "<br>",
"Meta-ecosystem: ", filtered_data$patch_type, "<br>",
"Tot Biomass: ", round(filtered_data$bioarea_mm2_per_ml, digits = 2), "<br>"),
hoverinfo = "text") %>%
plotly::layout(
xaxis = list(title = "Fitted Values"),
yaxis = list(title = "Residuals")
)Click for residual plots
res_vs_fitqqnorm(resid(reduced_model))
model_stats_fixed = compute.model.stats(reduced_model,
null_model,
"mixed_model")
model_stats_fixed %>%
mutate(deltaAIC = round(deltaAIC, digits = 1),
p_value = round(p_value, digits = 3),
R2 = NULL,
evidence = "",
evidence = ifelse(p_value > 0.1,
"none",
evidence),
evidence = ifelse(p_value < 0.1,
"* weak",
evidence),
evidence = ifelse(p_value < 0.05,
"** moderate",
evidence),
evidence = ifelse(p_value < 0.01,
"*** strong",
evidence),
evidence = ifelse(p_value < 0.001,
"**** very strong",
evidence),
p_value = ifelse(p_value < 0.001,
"< 0.001",
p_value)) %>%
print()## deltaAIC p_value evidence
## 1 -1.3 0.068 * weakresults_table = fill.results.table(
results_table,
response_variable,
patch_type_i,
model_stats_full,
model_stats_fixed
)Richness
response_variable = "species_richness"filtered_data = ds_patches %>%
filter(time_point >= first_time_point_model,
time_point <= last_time_point_model,
patch_type %in% patch_type_i,
!is.na(!!sym(response_variable)))plot.patches.points(filtered_data,
patch_type_i,
response_variable) 
null_model = lmer(
get(response_variable) ~
day +
(day | culture_ID),
data = filtered_data,
REML = FALSE,
control = lmerControl(optimizer = "Nelder_Mead")
)## Warning: Model failed to converge with 1 negative eigenvalue: -4.4e+00patch_type and
patch_type : day)
full_model = lmer(
get(response_variable) ~
day +
patch_type +
patch_type : day +
(day | culture_ID),
data = filtered_data,
REML = FALSE,
control = lmerControl(optimizer = "Nelder_Mead")
)## Warning: Model failed to converge with 1 negative eigenvalue: -7.2e-01Click for model output
print(summary(full_model), digits = 1)## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
## method [lmerModLmerTest]
## Formula: get(response_variable) ~ day + patch_type + patch_type:day +
## (day | culture_ID)
## Data: filtered_data
## Control: lmerControl(optimizer = "Nelder_Mead")
##
## AIC BIC logLik deviance df.resid
## 302.6 322.6 -143.3 286.6 82
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.1 -0.6 0.1 0.5 2.3
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## culture_ID (Intercept) 0e+00 0.00
## day 5e-04 0.02 NaN
## Residual 1e+00 1.13
## Number of obs: 90, groups: culture_ID, 15
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 7.55 0.58 72.97 13.0
## day -0.02 0.03 34.65 -0.7
## patch_typeLarge connected to large -0.34 0.71 72.97 -0.5
## day:patch_typeLarge connected to large -0.03 0.04 34.65 -0.7
## Pr(>|t|)
## (Intercept) <2e-16 ***
## day 0.5
## patch_typeLarge connected to large 0.6
## day:patch_typeLarge connected to large 0.5
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) day p_Lctl
## day -0.888
## ptch_tyLctl -0.816 0.725
## dy:ptc_Lctl 0.725 -0.816 -0.888
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')Click for model ANOVA
anova(full_model)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## day 3.9602 3.9602 1 34.648 3.1048 0.08688 .
## patch_type 0.2862 0.2862 1 72.965 0.2244 0.63713
## day:patch_type 0.5569 0.5569 1 34.648 0.4366 0.51313
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#Create resid vs fitted (if your number of fitted values is lower than the number of rows in the filtered dataset, it's probably because you have NAs)
res_vs_fit = filtered_data %>%
mutate(predicted = fitted(full_model),
residuals = resid(full_model)) %>%
plot_ly(x = ~predicted,
y = ~residuals,
type = "scatter",
mode = "markers",
marker = list(size = 5, color = "#4C78A8"),
text = paste(" System nr: ", filtered_data$culture_ID, "<br>",
"Day: ", filtered_data$day, "<br>",
"Meta-ecosystem: ", filtered_data$patch_type, "<br>",
"Tot Biomass: ", round(filtered_data$bioarea_mm2_per_ml, digits = 2), "<br>"),
hoverinfo = "text") %>%
plotly::layout(
xaxis = list(title = "Fitted Values"),
yaxis = list(title = "Residuals")
)Click for residual plots
res_vs_fitqqnorm(resid(full_model))
model_stats_full = compute.model.stats(full_model,
null_model,
"mixed_model")
model_stats_full %>%
mutate(deltaAIC = round(deltaAIC, digits = 1),
p_value = round(p_value, digits = 3),
R2 = NULL,
evidence = "",
evidence = ifelse(p_value > 0.1,
"none",
evidence),
evidence = ifelse(p_value < 0.1,
"* weak",
evidence),
evidence = ifelse(p_value < 0.05,
"** moderate",
evidence),
evidence = ifelse(p_value < 0.01,
"*** strong",
evidence),
evidence = ifelse(p_value < 0.001,
"**** very strong",
evidence),
p_value = ifelse(p_value < 0.001,
"< 0.001",
p_value)) %>%
print()## deltaAIC p_value evidence
## 1 -0.9 0.085 * weak
patch_type)
reduced_model = lmer(
get(response_variable) ~
day +
patch_type +
(day | culture_ID),
data = filtered_data,
REML = FALSE,
control = lmerControl(optimizer = "Nelder_Mead")
)Click for model output
print(summary(reduced_model), digits = 1)## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
## method [lmerModLmerTest]
## Formula: get(response_variable) ~ day + patch_type + (day | culture_ID)
## Data: filtered_data
## Control: lmerControl(optimizer = "Nelder_Mead")
##
## AIC BIC logLik deviance df.resid
## 301.0 318.5 -143.5 287.0 83
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.1 -0.6 0.2 0.5 2.3
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## culture_ID (Intercept) 0e+00 0.00
## day 5e-04 0.02 NaN
## Residual 1e+00 1.13
## Number of obs: 90, groups: culture_ID, 15
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 7.83 0.40 65.64 20 <2e-16
## day -0.04 0.02 31.73 -2 0.04
## patch_typeLarge connected to large -0.75 0.33 15.15 -2 0.04
##
## (Intercept) ***
## day *
## patch_typeLarge connected to large *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) day
## day -0.744
## ptch_tyLctl -0.545 0.000
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')#Create resid vs fitted
res_vs_fit = filtered_data %>%
mutate(predicted = fitted(reduced_model),
residuals = resid(reduced_model)) %>%
plot_ly(x = ~predicted,
y = ~residuals,
type = "scatter",
mode = "markers",
marker = list(size = 5, color = "#4C78A8"),
text = paste(" System nr: ", filtered_data$culture_ID, "<br>",
"Day: ", filtered_data$day, "<br>",
"Meta-ecosystem: ", filtered_data$patch_type, "<br>",
"Tot Biomass: ", round(filtered_data$bioarea_mm2_per_ml, digits = 2), "<br>"),
hoverinfo = "text") %>%
plotly::layout(
xaxis = list(title = "Fitted Values"),
yaxis = list(title = "Residuals")
)Click for residual plots
res_vs_fitqqnorm(resid(reduced_model))
model_stats_fixed = compute.model.stats(reduced_model,
null_model,
"mixed_model")
model_stats_fixed %>%
mutate(deltaAIC = round(deltaAIC, digits = 1),
p_value = round(p_value, digits = 3),
R2 = NULL,
evidence = "",
evidence = ifelse(p_value > 0.1,
"none",
evidence),
evidence = ifelse(p_value < 0.1,
"* weak",
evidence),
evidence = ifelse(p_value < 0.05,
"** moderate",
evidence),
evidence = ifelse(p_value < 0.01,
"*** strong",
evidence),
evidence = ifelse(p_value < 0.001,
"**** very strong",
evidence),
p_value = ifelse(p_value < 0.001,
"< 0.001",
p_value)) %>%
print()## deltaAIC p_value evidence
## 1 -2.5 0.034 ** moderateresults_table = fill.results.table(
results_table,
response_variable,
patch_type_i,
model_stats_full,
model_stats_fixed
)Evenness
response_variable = "evenness_pielou"filtered_data = ds_patches %>%
filter(time_point >= first_time_point_model,
time_point <= last_time_point_model,
patch_type %in% patch_type_i,
!is.na(!!sym(response_variable)))plot.patches.points(filtered_data,
patch_type_i,
response_variable) 
null_model = lmer(
get(response_variable) ~
day +
(day | culture_ID),
data = filtered_data,
REML = FALSE,
control = lmerControl(optimizer = "Nelder_Mead")
)patch_type and
patch_type : day)
full_model = lmer(
get(response_variable) ~
day +
patch_type +
patch_type : day +
(day | culture_ID),
data = filtered_data,
REML = FALSE,
control = lmerControl(optimizer = "Nelder_Mead")
)Click for model output
print(summary(full_model), digits = 1)## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
## method [lmerModLmerTest]
## Formula: get(response_variable) ~ day + patch_type + patch_type:day +
## (day | culture_ID)
## Data: filtered_data
## Control: lmerControl(optimizer = "Nelder_Mead")
##
## AIC BIC logLik deviance df.resid
## -155.5 -135.5 85.8 -171.5 82
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.4 -0.5 0.1 0.6 1.6
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## culture_ID (Intercept) 9e-03 0.095
## day 3e-05 0.006 -1.00
## Residual 8e-03 0.088
## Number of obs: 90, groups: culture_ID, 15
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 0.911 0.062 16.136 15
## day -0.007 0.003 15.077 -2
## patch_typeLarge connected to large -0.155 0.076 16.136 -2
## day:patch_typeLarge connected to large 0.009 0.004 15.077 2
## Pr(>|t|)
## (Intercept) 9e-11 ***
## day 0.05 .
## patch_typeLarge connected to large 0.06 .
## day:patch_typeLarge connected to large 0.05 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) day p_Lctl
## day -0.966
## ptch_tyLctl -0.816 0.789
## dy:ptc_Lctl 0.789 -0.816 -0.966
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')Click for model ANOVA
anova(full_model)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## day 0.013969 0.013969 1 15.077 1.8244 0.19672
## patch_type 0.032213 0.032213 1 16.136 4.2069 0.05686 .
## day:patch_type 0.033595 0.033595 1 15.077 4.3875 0.05350 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#Create resid vs fitted (if your number of fitted values is lower than the number of rows in the filtered dataset, it's probably because you have NAs)
res_vs_fit = filtered_data %>%
mutate(predicted = fitted(full_model),
residuals = resid(full_model)) %>%
plot_ly(x = ~predicted,
y = ~residuals,
type = "scatter",
mode = "markers",
marker = list(size = 5, color = "#4C78A8"),
text = paste(" System nr: ", filtered_data$culture_ID, "<br>",
"Day: ", filtered_data$day, "<br>",
"Meta-ecosystem: ", filtered_data$patch_type, "<br>",
"Tot Biomass: ", round(filtered_data$bioarea_mm2_per_ml, digits = 2), "<br>"),
hoverinfo = "text") %>%
plotly::layout(
xaxis = list(title = "Fitted Values"),
yaxis = list(title = "Residuals")
)Click for residual plots
res_vs_fitqqnorm(resid(full_model))
model_stats_full = compute.model.stats(full_model,
null_model,
"mixed_model")
model_stats_full %>%
mutate(deltaAIC = round(deltaAIC, digits = 1),
p_value = round(p_value, digits = 3),
R2 = NULL,
evidence = "",
evidence = ifelse(p_value > 0.1,
"none",
evidence),
evidence = ifelse(p_value < 0.1,
"* weak",
evidence),
evidence = ifelse(p_value < 0.05,
"** moderate",
evidence),
evidence = ifelse(p_value < 0.01,
"*** strong",
evidence),
evidence = ifelse(p_value < 0.001,
"**** very strong",
evidence),
p_value = ifelse(p_value < 0.001,
"< 0.001",
p_value)) %>%
print()## deltaAIC p_value evidence
## 1 0.1 0.145 none
patch_type)
reduced_model = lmer(
get(response_variable) ~
day +
patch_type +
(day | culture_ID),
data = filtered_data,
REML = FALSE,
control = lmerControl(optimizer = "Nelder_Mead")
)Click for model output
print(summary(reduced_model), digits = 1)## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
## method [lmerModLmerTest]
## Formula: get(response_variable) ~ day + patch_type + (day | culture_ID)
## Data: filtered_data
## Control: lmerControl(optimizer = "Nelder_Mead")
##
## AIC BIC logLik deviance df.resid
## -153.7 -136.2 83.8 -167.7 83
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.3 -0.5 0.1 0.7 1.6
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## culture_ID (Intercept) 1e-02 0.119
## day 5e-05 0.007 -1.00
## Residual 8e-03 0.088
## Number of obs: 90, groups: culture_ID, 15
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 0.809 0.042 18.815 19.1 9e-14
## day -0.001 0.002 15.031 -0.6 0.6
## patch_typeLarge connected to large -0.002 0.020 72.123 -0.1 0.9
##
## (Intercept) ***
## day
## patch_typeLarge connected to large
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) day
## day -0.925
## ptch_tyLctl -0.310 0.000
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')#Create resid vs fitted
res_vs_fit = filtered_data %>%
mutate(predicted = fitted(reduced_model),
residuals = resid(reduced_model)) %>%
plot_ly(x = ~predicted,
y = ~residuals,
type = "scatter",
mode = "markers",
marker = list(size = 5, color = "#4C78A8"),
text = paste(" System nr: ", filtered_data$culture_ID, "<br>",
"Day: ", filtered_data$day, "<br>",
"Meta-ecosystem: ", filtered_data$patch_type, "<br>",
"Tot Biomass: ", round(filtered_data$bioarea_mm2_per_ml, digits = 2), "<br>"),
hoverinfo = "text") %>%
plotly::layout(
xaxis = list(title = "Fitted Values"),
yaxis = list(title = "Residuals")
)Click for residual plots
res_vs_fitqqnorm(resid(reduced_model))
model_stats_fixed = compute.model.stats(reduced_model,
null_model,
"mixed_model")
model_stats_fixed %>%
mutate(deltaAIC = round(deltaAIC, digits = 1),
p_value = round(p_value, digits = 3),
R2 = NULL,
evidence = "",
evidence = ifelse(p_value > 0.1,
"none",
evidence),
evidence = ifelse(p_value < 0.1,
"* weak",
evidence),
evidence = ifelse(p_value < 0.05,
"** moderate",
evidence),
evidence = ifelse(p_value < 0.01,
"*** strong",
evidence),
evidence = ifelse(p_value < 0.001,
"**** very strong",
evidence),
p_value = ifelse(p_value < 0.001,
"< 0.001",
p_value)) %>%
print()## deltaAIC p_value evidence
## 1 2 0.914 noneresults_table = fill.results.table(
results_table,
response_variable,
patch_type_i,
model_stats_full,
model_stats_fixed
)Median Size
response_variable = "median_body_area_µm2"filtered_data = ds_patches %>%
filter(time_point >= first_time_point_model,
time_point <= last_time_point_model,
patch_type %in% patch_type_i,
!is.na(!!sym(response_variable)))plot.patches.points(filtered_data,
patch_type_i,
response_variable) 
null_model = lmer(
get(response_variable) ~
day +
(day | culture_ID),
data = filtered_data,
REML = FALSE,
control = lmerControl(optimizer = "Nelder_Mead")
)patch_type and
patch_type : day)
full_model = lmer(
get(response_variable) ~
day +
patch_type +
patch_type : day +
(day | culture_ID),
data = filtered_data,
REML = FALSE,
control = lmerControl(optimizer = "Nelder_Mead")
)Click for model output
print(summary(full_model), digits = 1)## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
## method [lmerModLmerTest]
## Formula: get(response_variable) ~ day + patch_type + patch_type:day +
## (day | culture_ID)
## Data: filtered_data
## Control: lmerControl(optimizer = "Nelder_Mead")
##
## AIC BIC logLik deviance df.resid
## 1383.4 1403.4 -683.7 1367.4 82
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.02 -0.62 -0.07 0.49 3.03
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## culture_ID (Intercept) 1e+05 315
## day 5e+02 22 -1.00
## Residual 2e+05 457
## Number of obs: 90, groups: culture_ID, 15
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 3236 274 24 12
## day 17 16 17 1
## patch_typeLarge connected to large -848 336 24 -2
## day:patch_typeLarge connected to large 54 19 17 3
## Pr(>|t|)
## (Intercept) 2e-11 ***
## day 0.29
## patch_typeLarge connected to large 0.02 *
## day:patch_typeLarge connected to large 0.01 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) day p_Lctl
## day -0.946
## ptch_tyLctl -0.816 0.773
## dy:ptc_Lctl 0.773 -0.816 -0.946
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')Click for model ANOVA
anova(full_model)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## day 4437335 4437335 1 17.105 21.2356 0.0002469 ***
## patch_type 1330309 1330309 1 23.488 6.3664 0.0188303 *
## day:patch_type 1646085 1646085 1 17.105 7.8776 0.0120813 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#Create resid vs fitted (if your number of fitted values is lower than the number of rows in the filtered dataset, it's probably because you have NAs)
res_vs_fit = filtered_data %>%
mutate(predicted = fitted(full_model),
residuals = resid(full_model)) %>%
plot_ly(x = ~predicted,
y = ~residuals,
type = "scatter",
mode = "markers",
marker = list(size = 5, color = "#4C78A8"),
text = paste(" System nr: ", filtered_data$culture_ID, "<br>",
"Day: ", filtered_data$day, "<br>",
"Meta-ecosystem: ", filtered_data$patch_type, "<br>",
"Tot Biomass: ", round(filtered_data$bioarea_mm2_per_ml, digits = 2), "<br>"),
hoverinfo = "text") %>%
plotly::layout(
xaxis = list(title = "Fitted Values"),
yaxis = list(title = "Residuals")
)Click for residual plots
res_vs_fitqqnorm(resid(full_model))
model_stats_full = compute.model.stats(full_model,
null_model,
"mixed_model")
model_stats_full %>%
mutate(deltaAIC = round(deltaAIC, digits = 1),
p_value = round(p_value, digits = 3),
R2 = NULL,
evidence = "",
evidence = ifelse(p_value > 0.1,
"none",
evidence),
evidence = ifelse(p_value < 0.1,
"* weak",
evidence),
evidence = ifelse(p_value < 0.05,
"** moderate",
evidence),
evidence = ifelse(p_value < 0.01,
"*** strong",
evidence),
evidence = ifelse(p_value < 0.001,
"**** very strong",
evidence),
p_value = ifelse(p_value < 0.001,
"< 0.001",
p_value)) %>%
print()## deltaAIC p_value evidence
## 1 -2.5 0.039 ** moderate
patch_type)
reduced_model = lmer(
get(response_variable) ~
day +
patch_type +
(day | culture_ID),
data = filtered_data,
REML = FALSE,
control = lmerControl(optimizer = "Nelder_Mead")
)Click for model output
print(summary(reduced_model), digits = 1)## Linear mixed model fit by maximum likelihood . t-tests use Satterthwaite's
## method [lmerModLmerTest]
## Formula: get(response_variable) ~ day + patch_type + (day | culture_ID)
## Data: filtered_data
## Control: lmerControl(optimizer = "Nelder_Mead")
##
## AIC BIC logLik deviance df.resid
## 1387.8 1405.3 -686.9 1373.8 83
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.9 -0.6 -0.2 0.5 3.0
##
## Random effects:
## Groups Name Variance Std.Dev. Corr
## culture_ID (Intercept) 2e+05 497
## day 1e+03 33 -1.00
## Residual 2e+05 459
## Number of obs: 90, groups: culture_ID, 15
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2651 201 24 13.2 2e-12
## day 54 11 16 4.8 2e-04
## patch_typeLarge connected to large 30 108 52 0.3 0.8
##
## (Intercept) ***
## day ***
## patch_typeLarge connected to large
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) day
## day -0.897
## ptch_tyLctl -0.360 0.000
## optimizer (Nelder_Mead) convergence code: 0 (OK)
## boundary (singular) fit: see help('isSingular')#Create resid vs fitted
res_vs_fit = filtered_data %>%
mutate(predicted = fitted(reduced_model),
residuals = resid(reduced_model)) %>%
plot_ly(x = ~predicted,
y = ~residuals,
type = "scatter",
mode = "markers",
marker = list(size = 5, color = "#4C78A8"),
text = paste(" System nr: ", filtered_data$culture_ID, "<br>",
"Day: ", filtered_data$day, "<br>",
"Meta-ecosystem: ", filtered_data$patch_type, "<br>",
"Tot Biomass: ", round(filtered_data$bioarea_mm2_per_ml, digits = 2), "<br>"),
hoverinfo = "text") %>%
plotly::layout(
xaxis = list(title = "Fitted Values"),
yaxis = list(title = "Residuals")
)Click for residual plots
res_vs_fitqqnorm(resid(reduced_model))
model_stats_fixed = compute.model.stats(reduced_model,
null_model,
"mixed_model")
model_stats_fixed %>%
mutate(deltaAIC = round(deltaAIC, digits = 1),
p_value = round(p_value, digits = 3),
R2 = NULL,
evidence = "",
evidence = ifelse(p_value > 0.1,
"none",
evidence),
evidence = ifelse(p_value < 0.1,
"* weak",
evidence),
evidence = ifelse(p_value < 0.05,
"** moderate",
evidence),
evidence = ifelse(p_value < 0.01,
"*** strong",
evidence),
evidence = ifelse(p_value < 0.001,
"**** very strong",
evidence),
p_value = ifelse(p_value < 0.001,
"< 0.001",
p_value)) %>%
print()## deltaAIC p_value evidence
## 1 1.9 0.808 noneresults_table = fill.results.table(
results_table,
response_variable,
patch_type_i,
model_stats_full,
model_stats_fixed
)Populations
for(patch_type_i in patch_type_i){
print(patch_type_i)
p = ds_patches %>%
filter(patch_type == patch_type_i) %>%
group_by(day) %>%
summarise(
Ble = mean(Ble_indiv_per_ml_dominance, na.rm = TRUE),
# Ble_se = sd(Ble_indiv_per_ml_dominance, na.rm = TRUE) / sqrt(n()),
# Ble_ci = qt(c(0.025, 0.975), df = n() - 1) * Ble_se,
Cep = mean(Cep_indiv_per_ml_dominance, na.rm = TRUE),
Col = mean(Col_indiv_per_ml_dominance, na.rm = TRUE),
Eug = mean(Eug_indiv_per_ml_dominance, na.rm = TRUE),
Eup = mean(Eup_indiv_per_ml_dominance, na.rm = TRUE),
Lox = mean(Lox_indiv_per_ml_dominance, na.rm = TRUE),
Pau = mean(Pau_indiv_per_ml_dominance, na.rm = TRUE),
Pca = mean(Pca_indiv_per_ml_dominance, na.rm = TRUE),
Spi = mean(Spi_indiv_per_ml_dominance, na.rm = TRUE),
Spi_te = mean(Spi_te_indiv_per_ml_dominance, na.rm = TRUE),
Tet = mean(Tet_indiv_per_ml_dominance, na.rm = TRUE)
) %>%
pivot_longer(Ble:Tet, names_to = "species", values_to = "species_indiv_per_ml") %>%
ggplot(aes(
x = day,
y = species_indiv_per_ml,
group = interaction(day, species),
color = species
)) +
geom_point(position = position_dodge(dodging),
size = treatment_points_size) +
geom_line(
aes(group = species),
position = position_dodge(dodging),
linewidth = treatment_lines_linewidth) +
labs(x = axis_names %>%
filter(variable == "day") %>%
pull(axis_name),
y = axis_names %>%
filter(variable == "dominance") %>%
pull(axis_name)) +
coord_cartesian(ylim = c(0, 100))
print(p)
}## [1] "Large connected to large"
## [1] "Large isolated"
Results
Meta-ecosystems
# Construct plots
response_variable_list = NULL
n_of_plots = 3
response_variable_list[[1]] = "mean_shannon"
response_variable_list[[2]] = "bray_curtis"
response_variable_list[[3]] = "metaecosystem_richness"
metaecosystem_type_i = c("Medium-Medium meta-ecosystem",
"Medium-Medium isolated",
"Small-Large meta-ecosystem",
"Small-Large isolated")
legend_row_n_input = 2
p = NULL
for (i in 1: n_of_plots) {
p[[i]] = plot.metaecos.points(ds_metaecosystems,
metaecosystem_type_i,
response_variable_list[[i]],
legend_row_n_input) +
theme(plot.margin = unit(c(ggarrange_margin_left,
ggarrange_margin_right,
ggarrange_margin_bottom,
ggarrange_margin_left),
"cm"))
}# Combine plots
p_combined = ggarrange(
p[[1]] +
rremove("xlab") +
theme(axis.text.x = element_blank(),
axis.ticks.x = element_blank()) +
font("legend.text", size = size_legend) +
font("ylab", size = size_y_axis),
p[[2]] +
rremove("xlab") +
theme(axis.text.x = element_blank(),
axis.ticks.x = element_blank()) +
font("legend.text", size = size_legend) +
font("ylab", size = size_y_axis),
p[[3]] +
font("legend.text", size = size_legend) +
font("xlab", size = size_y_axis) +
font("ylab", size = size_y_axis) +
scale_x_continuous(breaks = unique(ds_metaecosystems$day)),
heights = c(0.8,0.8,1),
nrow = 3,
common.legend = TRUE,
align = "v",
labels = c("(a)", "(b)", "(c)"),
label.x = 0.1,
label.y = 0.8
)# Show combined plots
p_combined
# Save combined plots
png(
file = here("results", "figures", disturbance_global_input, "paper", "metaecosystems_diversity.png"),
width = paper_width,
height = paper_height,
units = paper_units,
res = paper_res
)
p_combined
dev.off()# Plots parameters
legend_row_n_input = 2
x_min = -0.2
x_max = 30
y_min_alpha = 0.3
y_max_alpha = 2.1
y_min_beta = 0
y_max_beta = 1
size_y_axis = 22
metaecosystem_type_all = c("Medium-Medium isolated",
"Medium-Medium meta-ecosystem",
"Small-Large isolated",
"Small-Large meta-ecosystem")# Construct alpha plots
response_variable = "mean_shannon"
alpha_plots = NULL
alpha_plots[[1]] = plot.empty.plot.presentations(y_min_alpha,
y_max_alpha,
"mean_shannon")
for (i in 1:length(metaecosystem_type_all)) {
alpha_plots[[i+1]] = plot.metaecos.points.presentations(ds_metaecosystems,
metaecosystem_type_all[1:i],
response_variable,
legend_row_n_input) +
theme(plot.margin = unit(c(ggarrange_margin_left,
ggarrange_margin_right,
ggarrange_margin_bottom,
ggarrange_margin_left),
"cm"),
legend.position = "none") +
scale_y_continuous(labels = scales::number_format(accuracy = 0.01)) +
xlim(x_min, x_max) +
ylim(y_min_alpha, y_max_alpha)
}# Construct beta plots
response_variable = "bray_curtis"
beta_plots = NULL
beta_plots[[1]] = plot.empty.plot.presentations(y_min_beta,
y_max_beta,
"bray_curtis")
for (i in 1:length(metaecosystem_type_all)) {
beta_plots[[i+1]] = plot.metaecos.points.presentations(ds_metaecosystems,
metaecosystem_type_all[1:i],
response_variable,
legend_row_n_input) +
theme(plot.margin = unit(c(ggarrange_margin_left,
ggarrange_margin_right,
ggarrange_margin_bottom,
ggarrange_margin_left),
"cm"),
legend.position = "none") +
scale_y_continuous(labels = scales::number_format(accuracy = 0.01)) +
xlim(x_min, x_max) +
ylim(y_min_beta, y_max_beta)
}# Combine plots
p_combined = NULL
alpha_beta_plot_number = data.frame(alpha = as.double(c("1", "2", "3", "4", "5", "5", "5", "5", "5")),
beta = as.double(c("1", "1", "1", "1", "1", "2", "3", "4", "5")))
for(i in 1:length(alpha_beta_plot_number$alpha)){
p_combined[[i]] = ggarrange(
alpha_plots[[alpha_beta_plot_number$alpha[i]]] +
rremove("xlab") +
theme(axis.text.x = element_blank(),
axis.ticks.x = element_blank(),
axis.text = element_text(size = presentation_axes_size)) +
font("legend.text", size = size_legend) +
font("ylab", size = size_y_axis),
NULL,
beta_plots[[alpha_beta_plot_number$beta[i]]] +
theme(axis.text = element_text(size = presentation_axes_size)) +
font("legend.text", size = size_legend) +
font("xlab", size = size_y_axis) +
font("ylab", size = size_y_axis),
heights = c(0.999, 0.001, 0.999),
nrow = 3,
align = "v",
label.x = 0.1,
label.y = 0.8)
}# Save combined plots
for(i in 1:length(alpha_beta_plot_number$alpha)){
png(file = here("results", "figures", disturbance_global_input, "presentations", paste0(i, "_metaecos_diversity.png")),
width = presentation_figure_width,
height = presentation_figure_height,
units = presentation_figure_units,
res = presentation_figure_res)
print(p_combined[[i]])
dev.off()
}Isolated patches
# Construct plots
response_variable_list = NULL
response_variable_list[[1]] = "bioarea_mm2_per_ml"
response_variable_list[[2]] = "shannon"
patch_type_i = c("Small isolated",
"Medium isolated",
"Large isolated")
legend_row_n_input = 2
p = NULL
for (i in 1:2) {
p[[i]] = plot.patches.points(ds_patches,
patch_type_i,
response_variable_list[[i]],
legend_row_n_input) +
theme(plot.margin = unit(c(ggarrange_margin_left,
ggarrange_margin_right,
ggarrange_margin_bottom,
ggarrange_margin_left),
"cm"))
}# Combine plots
p_combined = ggarrange(
p[[1]] +
rremove("xlab") +
theme(axis.text.x = element_blank(),
axis.ticks.x = element_blank()) +
font("legend.text", size = size_legend) +
font("ylab", size = size_y_axis),
p[[2]] +
font("legend.text", size = size_legend) +
font("xlab", size = size_y_axis) +
font("ylab", size = size_y_axis) +
scale_x_continuous(breaks = unique(ds_patches$day)),
heights = c(0.8, 0.8, 1),
nrow = 2,
align = "v",
labels = c("(a)", "(b)"),
label.x = 0.1,
label.y = 0.8,
common.legend = TRUE
)# Show combined plots
p_combined
# Save combined plots
png(
file = here("results", "figures", disturbance_global_input, "paper", "patches_biomass.png"),
width = paper_width,
height = paper_height,
units = paper_units,
res = paper_res
)
p_combined
dev.off()Evaporation
Evaporation when microwaving 15 falcon tubes at the time
evaporation.test = read.csv(here("data", "evaporation_test","evaporation_test_right.csv"), header = TRUE)
evaporation.test %>%
ggplot(aes (x = as.character(water_pipetted),
y = weight_water_evaporated,
group = interaction(water_pipetted, as.character(rack)),
fill = as.character(rack))) +
geom_boxplot(width = boxplot_width) +
labs(x = "Water volume (ml)" ,
y = "Evaporation (g)",
fill = "Rack replicate")
Evaporation when microwaving 5 tubes with 10 filled or empty tubes
evaporation.test = read.csv(here("data", "evaporation_test", "evaporation_test_fill_nofill.csv"), header = TRUE)
evaporation.test %>%
ggplot(aes (x = all_tubes_water,
y = weight_water_evaporated)) +
geom_boxplot(width = boxplot_width) +
labs(x = "Water in the other 10 tubes" ,
y = "Evaporation (g)",
caption = "When all tubes were filled, they were filled with 6.75 ml of deionised water.")
Analysis scripts
Video analysis
rm(list = ls())
setwd("/media/mendel-himself/ID_061_Ema2/PatchSizePilot/training")
# load package
# library(devtools)
# install_github("femoerman/bemovi", ref="master")
library(bemovi)
library(parallel)
library(doParallel)
library(foreach)
#Define memory to be allocated
memory.alloc <- 240000 #-needs_to_be_specified
memory.per.identifier <- 40000 #-needs_to_be_specified
memory.per.linker <- 5000 #-needs_to_be_specified
memory.per.overlay <- 60000 #-needs_to_be_specified
# UNIX
# set paths to tools folder and particle linker
tools.path <-
"/home/mendel-himself/bemovi_tools/" #-needs_to_be_specified
to.particlelinker <- tools.path
# directories and file names
to.data <- paste(getwd(), "/", sep = "")
video.description.folder <- "0_video_description/"
video.description.file <- "video_description.txt"
raw.video.folder <- "1_raw/"
raw.avi.folder <- "1a_raw_avi/"
metadata.folder <- "1b_raw_meta/"
particle.data.folder <- "2_particle_data/"
trajectory.data.folder <- "3_trajectory_data/"
temp.overlay.folder <- "4a_temp_overlays/"
overlay.folder <- "4_overlays/"
merged.data.folder <- "5_merged_data/"
ijmacs.folder <- "ijmacs/"
######################################################################
# VIDEO PARAMETERS
# video frame rate (in frames per second)
fps <- 25 #-needs_to_be_specified
# length of video (in frames)
total_frames <- 125 #-needs_to_be_specified
#Dimensions of the videos in pixels
width = 2048 #-needs_to_be_specified
height = 2048 #-needs_to_be_specified
# measured volume (in microliter) #-needs_to_be_specified
measured_volume <-
34.4 # for Leica M205 C with 1.6 fold magnification, sample height 0.5 mm and Hamamatsu Orca Flash 4
#measured_volume <- 14.9 # for Nikon SMZ1500 with 2 fold magnification, sample height 0.5 mm and Canon 5D Mark III
# size of a pixel (in micrometer) #-needs_to_be_specified
pixel_to_scale <-
4.05 # for Leica M205 C with 1.6 fold magnification, sample height 0.5 mm and Hamamatsu Orca Flash 4
#pixel_to_scale <- 3.79 # for Nikon SMZ1500 with 2 fold magnification, sample height 0.5 mm and Canon 5D Mark III
# specify video file format (one of "avi","cxd","mov","tiff")
# bemovi only works with avi and cxd. other formats are reformated to avi below
video.format <- "cxd" #-needs_to_be_specified
# setup
difference.lag <- 10
thresholds <- c(13, 255) # don't change the second value
# thresholds <- c(50,255)
# MORE PARAMETERS (USUALLY NOT CHANGED)
######################################################################
# FILTERING PARAMETERS
# optimized for Perfex Pro 10 stereomicrocope with Perfex SC38800 (IDS UI-3880LE-M-GL) camera
# tested stereomicroscopes: Perfex Pro 10, Nikon SMZ1500, Leica M205 C
# tested cameras: Perfex SC38800, Canon 5D Mark III, Hamamatsu Orca Flash 4
# tested species: Tet, Col, Pau, Pca, Eug, Chi, Ble, Ceph, Lox, Spi
# min and max size: area in pixels
particle_min_size <- 10
particle_max_size <- 1000
# number of adjacent frames to be considered for linking particles
trajectory_link_range <- 3
# maximum distance a particle can move between two frames
trajectory_displacement <- 16
# these values are in the units defined by the parameters above: fps (seconds), measured_volume (microliters) and pixel_to_scale (micometers)
filter_min_net_disp <- 25
filter_min_duration <- 1
filter_detection_freq <- 0.1
filter_median_step_length <- 3
######################################################################
# VIDEO ANALYSIS
#Check if all tools are installed, and if not install them
check_tools_folder(tools.path)
#Ensure computer has permission to run bftools
system(paste0("chmod a+x ", tools.path, "bftools/bf.sh"))
system(paste0("chmod a+x ", tools.path, "bftools/bfconvert"))
system(paste0("chmod a+x ", tools.path, "bftools/showinf"))
# Convert files to compressed avi (takes approx. 2.25 minutes per video)
convert_to_avi(
to.data,
raw.video.folder,
raw.avi.folder,
metadata.folder,
tools.path,
fps,
video.format
)
# TESTING
# check file format and naming
# check_video_file_names(to.data,raw.avi.folder,video.description.folder,video.description.file)
# check whether the thresholds make sense (set "dark backgroud" and "red")
#check_threshold_values(to.data, raw.avi.folder, ijmacs.folder, 2, difference.lag, thresholds, tools.path, memory.alloc)
# identify particles
locate_and_measure_particles(
to.data,
raw.avi.folder,
particle.data.folder,
difference.lag,
min_size = particle_min_size,
max_size = particle_max_size,
thresholds = thresholds,
tools.path,
memory = memory.alloc,
memory.per.identifier = memory.per.identifier,
max.cores = detectCores() - 1
)
# link the particles
link_particles(
to.data,
particle.data.folder,
trajectory.data.folder,
linkrange = trajectory_link_range,
disp = trajectory_displacement,
start_vid = 1,
memory = memory.alloc,
memory_per_linkerProcess = memory.per.linker,
raw.avi.folder,
max.cores = detectCores() - 1,
max_time = 1
)
# merge info from description file and data
merge_data(
to.data,
particle.data.folder,
trajectory.data.folder,
video.description.folder,
video.description.file,
merged.data.folder
)
# load the merged data
load(paste0(to.data, merged.data.folder, "Master.RData"))
# filter data: minimum net displacement, their duration, the detection frequency and the median step length
trajectory.data.filtered <-
filter_data(
trajectory.data,
filter_min_net_disp,
filter_min_duration,
filter_detection_freq,
filter_median_step_length
)
# summarize trajectory data to individual-based data
morph_mvt <-
summarize_trajectories(
trajectory.data.filtered,
calculate.median = F,
write = T,
to.data,
merged.data.folder
)
# get sample level info
summarize_populations(
trajectory.data.filtered,
morph_mvt,
write = T,
to.data,
merged.data.folder,
video.description.folder,
video.description.file,
total_frames
)
# create overlays for validation
create.subtitle.overlays(
to.data,
traj.data = trajectory.data.filtered,
raw.video.folder,
raw.avi.folder,
temp.overlay.folder,
overlay.folder,
fps,
vid.length = total_frames / fps,
width,
height,
tools.path = tools.path,
overlay.type = "number",
video.format
)
# Create overlays (old method)
create_overlays(
traj.data = trajectory.data.filtered,
to.data = to.data,
merged.data.folder = merged.data.folder,
raw.video.folder = raw.avi.folder,
temp.overlay.folder = "4a_temp_overlays_old/",
overlay.folder = "4_overlays_old/",
width = width,
height = height,
difference.lag = difference.lag,
type = "traj",
predict_spec = F,
contrast.enhancement = 1,
IJ.path = "/home/mendel-himself/bemovi_tools",
memory = memory.alloc,
max.cores = detectCores() - 1,
memory.per.overlay = memory.per.overlay
)
########################################################################
# some cleaning up
#system("rm -r 2_particle_data")
#system("rm -r 3_trajectory_data")
#system("rm -r 4a_temp_overlays")
system("rm -r ijmacs")
########################################################################Species identification
rm(list = ls())
#install.packages("e1071",dependencies = T)
#install.packages("devtools",dependencies = T)
#install_github("pennekampster/bemovi", ref="master")
#library(devtools)
library(bemovi)
library(e1071)
library("here")
library("tidyverse")
time_points_in_experiment = c("t0", "t1", "t2", "t3", "t4", "t5", "t6", "t7")
for (time_point in time_points_in_experiment) {
#Folder names and paths
video.description.folder = "0_video_description/"
video.description.file = "video_description.txt"
merged.data.folder = "5_merged_data/"
monocultures_folder_path = here("biomass_analysis", "training", "")
mixed_cultures_folder_path = here("biomass_analysis", time_point, "")
#Parameters used in the video analysis script
fps = 25
nsv = 5
measured_volume = 34.4
pixel_to_scale = 4.05
filter_min_net_disp = 25
filter_min_duration = 1
filter_detection_freq = 0.1
filter_median_step_length = 3
#Load master dataset of mono-cultures
load(paste0(
monocultures_folder_path,
merged.data.folder,
"Master.RData"
))
trajectory.data_monocultures = trajectory.data
rm(trajectory.data)
#Filter the master data of mono-cultures using the same parameters as in the videos analysis script
trajectory.data_monocultures.filtered = filter_data(
trajectory.data_monocultures,
filter_min_net_disp,
filter_min_duration,
filter_detection_freq,
filter_median_step_length
)
#Go from trajectory data to individuals data
morph_mvt = summarize_trajectories(
data = trajectory.data_monocultures.filtered,
calculate.median = FALSE,
write = TRUE,
to.data = monocultures_folder_path,
merged.data.folder = merged.data.folder
) %>%
mutate(comment = NULL)
training_data = morph_mvt[complete.cases(morph_mvt), ]
svm1 = svm(
factor(species) ~
mean_grey +
sd_grey +
mean_area +
sd_area +
mean_perimeter +
mean_turning +
sd_turning +
sd_perimeter +
mean_major +
sd_major +
mean_minor +
sd_minor +
mean_ar +
sd_ar +
duration +
max_net +
net_disp +
net_speed +
gross_disp +
max_step +
min_step +
sd_step +
sd_gross_speed +
max_gross_speed +
min_gross_speed ,
data = training_data,
probability = T,
na.action = na.pass
)
confusion.matrix = table(svm1$fitted, training_data$species)
confusion.matrix.nd = confusion.matrix
diag(confusion.matrix.nd) = 0
svm1$confusion = cbind(confusion.matrix,
class.error = rowSums(confusion.matrix.nd) / rowSums(confusion.matrix))
print(paste("Confusion matrix of time point", time_point))
print(svm1$confusion)
species.names = unique(trajectory.data_monocultures$species)
load(paste0(
mixed_cultures_folder_path,
merged.data.folder,
"Master.RData"
))
trajectory.data_mixed = trajectory.data
rm(trajectory.data)
trajectory.data_mixed.filtered = filter_data(
trajectory.data_mixed,
filter_min_net_disp,
filter_min_duration,
filter_detection_freq,
filter_median_step_length
)
morph_mvt = summarize_trajectories(
data = trajectory.data_mixed.filtered,
calculate.median = FALSE,
write = TRUE,
to.data = mixed_cultures_folder_path,
merged.data.folder = merged.data.folder
)[, which(colnames(morph_mvt) != "Col_manual")] %>%
mutate(comment = NULL)
data.to.predict = morph_mvt[complete.cases(morph_mvt),]
p.id = predict(object = svm1,
data.to.predict,
type = "response")
data.to.predict$predicted_species = as.character(p.id)
pop.data = summarize_populations(
traj.data = trajectory.data_monocultures.filtered,
sum.data = morph_mvt,
write = TRUE,
to.data = mixed_cultures_folder_path,
merged.data.folder = merged.data.folder,
video.description.folder = video.description.folder,
video.description.file = video.description.file,
total_frame = fps * nsv
)
species.density = function(sample_output,
indiv_predicted,
species_names,
total_frames,
mv = measured_volume) {
samples = unique(indiv_predicted$file)
sp.dens = matrix(0,
nrow(sample_output),
length(species_names))
colnames(sp.dens) = species_names
for (i in 1:length(samples)) {
indiv = subset(indiv_predicted, file == samples[i])
spec = unique(indiv$predicted_species)
for (j in 1:length(spec)) {
all.indiv.sp = subset(indiv,
predicted_species == spec[j])
dens = sum(all.indiv.sp$N_frames) / total_frames / mv
sp.dens[which(sample_output$file == as.character(samples[i])), which(species_names == spec[j])] = dens
}
}
return(cbind(sample_output, sp.dens))
}
output = species.density(
pop.data,
data.to.predict,
species.names,
total_frames = fps * nsv,
mv = measured_volume
)
file_name = paste0("species_ID_", time_point, ".csv")
write.csv(output,
here("biomass_analysis", "species_ID_results", file_name))
rm(output)
}Other
Running time
## Time difference of 1.8 minsDisturbance input
Check that disturbance_global_input is what you set:
print(paste0("Disturbance = ", disturbance_global_input))## [1] "Disturbance = low"Change code using terminal
If you want to change a certain part of the code:
#Rmd script
cd /Users/ema/Documents/GitHub/PatchSize/r_files
sed -i '' 's/old_string/new_string/g' *.Rmd
#R script
cd /Users/ema/Documents/GitHub/PatchSize/r_files/functions
sed -i '' 's/old_string/new_string/g' *.RCode to give you back an object
dput()Crashed cultures
The only type of patch where all cultures crashed was small connected to small at high disturbance.